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Citation: Ersoz, A.B.; Pekcan, O.;
Akbas, E. AIDCON: An Aerial Image
Dataset and Benchmark for
Construction Machinery. Remote Sens.
2024,16, 3295. https://doi.org/
10.3390/rs16173295
Academic Editors: Fabio Tosti,
Andrea Benedetto and
Deodato Tapete
Received: 16 July 2024
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remote sensing
Article
AIDCON: An Aerial Image Dataset and Benchmark for
Construction Machinery
Ahmet Bahaddin Ersoz 1, * , Onur Pekcan 1and Emre Akbas 2
1Department of Civil Engineering, Middle East Technical University, 06800 Ankara, Türkiye;
opekcan@metu.edu.tr
2Department of Computer Engineering, Middle East Technical University, 06800 Ankara, Türkiye;
emre@ceng.metu.edu.tr
*Correspondence: abersoz@metu.edu.tr
Abstract: Applying deep learning algorithms in the construction industry holds tremendous potential
for enhancing site management, safety, and efficiency. The development of such algorithms necessi-
tates a comprehensive and diverse image dataset. This study introduces the Aerial Image Dataset for
Construction (AIDCON), a novel aerial image collection containing 9563 construction machines across
nine categories annotated at the pixel level, carrying critical value for researchers and professionals
seeking to develop and refine object detection and segmentation algorithms across various construc-
tion projects. The study highlights the benefits of utilizing UAV-captured images by evaluating the
performance of five cutting-edge deep learning algorithms—Mask R-CNN, Cascade Mask R-CNN,
Mask Scoring R-CNN, Hybrid Task Cascade, and Pointrend—on the AIDCON dataset. It underscores
the significance of clustering strategies for generating reliable and robust outcomes. The AIDCON
dataset’s unique aerial perspective aids in reducing occlusions and provides comprehensive site
overviews, facilitating better object positioning and segmentation. The findings presented in this
paper have far-reaching implications for the construction industry, as they enhance construction
site efficiency while setting the stage for future advancements in construction site monitoring and
management utilizing remote sensing technologies.
Keywords: construction machinery; image dataset; unmanned aerial vehicle; deep learning; object
segmentation
1. Introduction
Traditional construction monitoring and management methods are based on man-
ual interpretations, which are labor-intensive and costly. Therefore, they are not suitable
for large-scale constructions. Automated methods have replaced manual ones with the
development and accessibility of remote sensing and data collection technologies. Many
data-gathering devices are tested and reported in the literature. Compared to other tech-
nologies like laser scanners and radiofrequency-based methods (RFID, Wi-Fi, UWB), digital
cameras are easy to use and require fewer human resources [
1
]. Prior research has demon-
strated that automated processes based on digital images increase productivity, decrease
safety risks, and speed up processes [
2
–
4
]. They support project managers in cost estimation,
resource allocation, and work schedules.
In addition to developing data collection methods, recent advancements in deep
learning (DL) algorithms have shown superior performance for object detection and track-
ing [
5
]. The availability of large datasets such as COCO [
6
] and Pascal VOC [
7
] drew
academics’ attention to the need for DL-based solutions for conventional applications. For
the construction fields, several vision-based DL applications have been developed, aided
by Convolutional Neural Networks (CNNs) that facilitate the automatic identification of
working activities of excavators, dump trucks, and workers [
8
]. Consequently, this allows
Remote Sens. 2024,16, 3295. https://doi.org/10.3390/rs16173295 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2024,16, 3295 2 of 22
for surveilling machinery productivity during site operations [
9
]. Additionally, the auto-
matic detection of equipment enables the measurement of proximities among construction
entities, promoting a safer working environment for construction workers [
10
]. Identifying
workers and their hardhat usage is a crucial safety measure that protects construction
workers from accidents [11].
Vision-based construction applications highly rely on the accurate detection of con-
struction objects. Accurate detection can be achieved using a well-established dataset and
a robust DL algorithm. DL algorithms demonstrated their effectiveness on the benchmark
datasets. However, there are few open datasets available for construction. Most research in
the construction industry uses datasets unique to the suggested strategy, and most of them
are not accessible to other researchers. The majority of them were gathered through CCTV
cameras and mobile device cameras.
On the other hand, unlike ground-level devices, Unmanned Aerial Vehicles (UAVs)
offer several advantages. First, UAV-based aerial imaging enables views of a wide range
of hard-to-reach areas quickly and effectively, which is comparably difficult or sometimes
impossible for ground-level conventional devices [
12
,
13
]. Ground cameras, due to their
fixed positions, typically require more time and effort to survey large areas, which can be
a major drawback in extensive projects. Second, obstacles like buildings, trees, or large
equipment can hinder ground camera view and create data gaps [
14
,
15
]. UAV technology
effectively prevents occlusion problems from frequently happening at the site. From
this perspective, static ground cameras lack the flexibility and comprehensive coverage
that UAVs provide. In addition, a top-down view of a construction site allows for precise
proximity monitoring of construction objects. UAVs equipped with high-resolution cameras
and GPS allow for aerial-guided positioning of workers, equipment, and machines [10]
In contrast to static ground cameras, which require permanent installation and main-
tenance on-site, UAVs do not need fixed setups, which reduces the logistical burden and
costs of maintaining a network of cameras. UAVs can be rapidly deployed and repositioned
as needed, offering greater adaptability to changing site conditions compared to the fixed
positions of ground cameras. While continuous monitoring is not possible with UAVs,
periodic flights can be strategically scheduled to capture critical stages of the construction
process. This targeted approach ensures that key milestones are documented without the
need for constant surveillance. The use of UAVs was, therefore, intended to complement,
rather than replace, traditional ground-based methods, providing critical insights at the
key stages of construction. Although there are several applications for UAV imagery in the
construction field, there is a limited and less open dataset of aerial images considering the
current literature.
In this study, we present an open aerial image dataset named the Aerial Image Dataset
for Construction (AIDCON), including nine object types, namely, dump truck, excava-
tor, backhoe loader, wheel loader, compactor, dozer, grader, car, and other construction
machinery grouped in a particular category. A total of 2155 images were collected from
25 different locations via UAV bird’s-eye view from various cities of Türkiye. The aerial
images were captured from multiple sites, including excavation fields, steel structures,
reinforced concrete structures, transportation projects, and parking areas designated for
construction machinery. An intense amount of work has been performed to annotate
construction machines at the pixel level. In total, 9563 objects with their boundaries have
been annotated in the images rather than the commonly used bounding box annotation
method. Providing machine layouts enables us to understand the poses and activities of
machines. Image similarity index and location-based clustering were proposed to group
images for appropriate train-test splitting. Finally, the performance of the dataset has
been evaluated using five different DL-based instance segmentation algorithms, including
Mask R-CNN [
16
], Cascade Mask R-CNN [
17
], Mask Scoring R-CNN [
18
], Hybrid Task
Cascade [
19
], and Pointrend [
20
]. A webpage was created to provide open access to the
dataset and facilitate future research contributions.
Remote Sens. 2024,16, 3295 3 of 22
2. Literature Review
This literature review examines the crucial role of large-scale datasets in advancing
computer vision applications, especially within the construction industry. It starts with
general-purpose datasets before shifting focus to construction-specific datasets, empha-
sizing their unique challenges and innovative data capture methods. These datasets are
essential for developing techniques that enhance object detection, productivity measure-
ment, and safety monitoring in construction settings. This review highlights the evolution
and significance of these datasets and their broad applications across various domains.
2.1. General-Purpose Datasets
The availability of large-scale datasets is crucial in developing and evaluating deep
learning algorithms. One of the first open datasets was published by the California Institute
of Technology researchers. The Caltech-101 [
21
] and Caltech-256 [
22
] datasets, as their
names imply, contain 101 and 256 classes (such as cars, motorbikes, airplanes), 9146 and
30,607 photos, respectively. Similarly, the CIFAR-10 and CIFAR-100 datasets have 10 and
100 classes of 60,000 32
×
32 images [
23
]. The Pascal Visual Object Classes (Pascal VOC)
challenge [
7
] is one of the significant competitions in early computer vision research. The
challenge datasets were published between 2010 and 2015. It started with four classes and
1578 images and eventually ended up with 20 categories and 11,530 images. A variety of
classes are labeled, such as the chair, dog, and person. Microsoft COCO (Common Objects
in Context) dataset [
6
] is among the most used image datasets, containing 330,000 images
with more than 200,000 labels. Images, tiny objects compared to Pascal VOC, were labeled
in 80 categories. ImageNet [
24
] and Open Images Dataset by Google [
25
] are large-scale
datasets with 14 million and 9 million images with thousands of classes, from balloons to
strawberries. Recently, Facebook AI Researchers published the LVIS (Large Vocabulary
Instance Segmentation) dataset with a higher number of categories—over 1000 entry-level
object categories—compared to previous ones [
26
]. Following a meticulous approach,
2 million items were masked in 164,000 images.
2.2. Construction-Specific Datasets
In construction, one of the first standard datasets was created by Tajeen and Zhu [
27
].
It covers five pieces of construction equipment (excavator, loader, dozer, roller, and back-
hoe) with various viewpoints, poses, and sizes in 2000 images. Not very popular when the
dataset was created, deep learning detectors were not used to evaluate dataset performance,
and the images were not provided to the public. Kim et al. [
28
] proposed a benchmark
dataset, the so-called Advanced Infrastructure Management (AIM) dataset, filtering con-
struction equipment in the ImageNet database. Performance evaluation was conducted
using Faster R-CNN with ResNet-101. This study shows the capability of deep learning
algorithms for detecting heavy construction equipment. An image dataset particularly
developed for construction equipment, Alberta Construction Image Dataset (ACID), was
published by Xiao and Kang [
29
]. ACID contains 10,000 images collected from the ground
and in ten construction object classes. The dataset was trained by YOLO-v3, Inception-SSD,
R-FCN-ResNet101, and Faster-R-CNN-ResNet101 to test its feasibility. Xuehui et al. [
30
]
presented the Moving Objects in Construction Sites (MOCS) image dataset that contains
41,668 images and 13 categories of pixel-level annotations. Deep learning algorithms were
used for benchmark analysis, object detection, and instance segmentation. Unlike the ACID
dataset, MOCS has UAV footage images. The images are captured from low altitudes
and collected around the construction machines, but no top-view pictures are provided
in the dataset. Duan et al. [
15
] developed a Site Object Detection Dataset (SODA) that
contains 15 object classes, such as scaffold, hook, and fence, which do not overlap with
existing construction datasets. The aim is to create a distinct dataset that can be used for
a wide range of construction applications, but SODA does not include any construction
machinery categories. Del Savio et al. [
31
] presented a dataset of 1046 images from four
static cameras around a construction site. The images were manually classified into eight
Remote Sens. 2024,16, 3295 4 of 22
object classes commonly found in a construction environment. Recently, Yan et al. [
32
]
created one of the most extensive datasets, the Construction Instance Segmentation (CIS)
dataset. This significant contribution includes 50,000 images with ten object categories and
104,021 annotated instances. This dataset spans a wide range of construction sites and is
taken with various imaging equipment. It also includes many construction aspects, such
as workers wearing and not wearing safety helmets and different types of construction
trucks. These datasets can be helpful for developing computer vision techniques in the
engineering and construction fields. A comparison of existing datasets with our proposed
dataset AIDCON is tabulated in Table 1.
Table 1. Comparison of datasets.
Tajeen et al. [27] AIM [28] ACID [29] MOCS [30]
Del Savio et al. [
31
]
CIS [32] AIDCON
Year 2014 2018 2021 2021 2022 2023 2024
No. of Machinery
Categories 5 5 10 11 7 7 8
No. of Images 2000 2920 10,000 41,668 1046 50,000 2155
Instances per
Image 1 1 1.58 5.34 N/A 2.08 4.34
Ratio of Aerial
Images 0 N/A 0.50% N/A 0 N/A 100%
Image Source On-site ImageNet On-site + Web On-site On-site On-site + Web On-site
Devices Digital cameras N/A
Cell phone
cameras, UAVs,
on-site cameras
Smartphones,
UAVs, digital
cameras
Static cameras
Smartphones,
UAVs, digital and
security cameras
UAVs
Type of
Annotation Bounding Box Bounding Box Bounding Box Pixel-wise Bounding Box Pixel-wise Pixel-wise
Clustering Strategy
No No No No No No Yes
N/A: Not provided in the dataset.
Since manually gathering and annotating a huge image dataset takes much time,
synthetic datasets were presented in the literature. Soltani et al. [
33
] and Barrera-Animas
and Davila Delgado [
34
] created a dataset from the 3D models of construction machines
combined with various background images taken from construction sites. Bang et al. [
35
]
proposed a method of image augmentation that is based on cut and paste and image
inpainting techniques to create variations of original images. Hwang et al. [
36
,
37
] used
web crawling-based image collection to build a large dataset. The dataset includes only a
limited number of equipment types. The techniques of synthetically developing datasets
are not reliable enough due to the conditions and terrain of the construction, and further
customization is required.
2.3. Applications in Construction
The datasets concerning construction equipment available in the literature provide
various opportunities for application areas. Recognizing equipment from the images helps
to perform productivity measurement, performance control, and proactive work-zone
safety applications. Detection of equipment is the first step of these applications [
38
]. In
this regard, Faster R-CNN, a deep learning algorithm, has been adapted to detect people
and objects associated with a construction site, such as workers, excavators, and dump
trucks [39–41].
According to Golparvar-Fard et al. [
42
], a multi-class SVM classifier can recognize
excavator and dump truck actions from images captured by a fixed camera. Zhu et al. [
43
]
reported high precision and recall in identifying construction workers and equipment.
Another study by Luo et al. [
44
] presented a technique capable of detecting 22 construction-
related objects and 17 types of construction activities. Roberts and Golparvar-Fard [
8
]
introduced a method for identifying the operational activities of excavators and dump
trucks using the AIM dataset. Additionally, Xiao and Kang [
9
] proposed the construction
Remote Sens. 2024,16, 3295 5 of 22
machine tracker (CMT) system to track multiple construction machines based on a method
of image hashing features and assisted by the ACID dataset.
By leveraging object detection and tracking algorithms, the productivity of construc-
tion equipment, such as working cycles, cycle times, and delays, can be recognized and
measured [
2
,
45
,
46
]. Kim et al. [
47
] estimated earthwork productivity using CNN-based
excavator and dump truck detection. Knowing the equipment’s pose from a productivity
perspective makes it easier to calculate the time the operator spent on each stage of their
activities [
4
]. Soltani et al. [
48
] introduced a method to process each camera from the job
site and create the 2D skeleton of the excavator; then, the relative rotation and translation
data between the cameras’ coordinate systems were used to determine the 3D posture. A
synthetic dataset was created by Mahmood et al. [
49
] to train a vision-based model that
predicts the 3D posture of an excavator. 3D pose detection helps to monitor safety and
excavator activity analysis [
3
]. Chi and Caldas [
50
] and Rezazadeh Azar and McCabe [
51
]
presented object recognition and background subtraction algorithms based on proactive
work-zone safety automation. Rezazadeh Azar et al. [
52
] and Kim et al. [
53
] created a
framework for identifying vision-based activities that consider interactive features of earth-
moving machinery operation. Utilizing a deep active learning methodology and a few-shot
learning strategy can significantly reduce the number of images required for training
purposes [
54
,
55
]. By including a new machine class and suggesting an enhanced version
of the SSD MobileNet object detector appropriate for embedded devices,
Arabi et al. [56]
enhanced the AIM construction machine dataset.
With the introduction of UAVs, equipment detection and tracking were widely used in
aerial imaging. Guo et al. [
57
] suggested using UAV images to identify numerous construc-
tion machines with orientation-aware feature fusion single-stage detection (OAFF-SSD).
Meanwhile, Meng et al. [
58
] proposed a real-time detection of excavators for pipeline
safety utilizing the widely used YOLO v3 algorithm and 350 collected images via a UAV
DJI M600 Pro. Since aerial views enable information to be obtained from larger areas
compared to ground images, UAVs are mainly utilized for proximity monitoring applica-
tions.
Kim et al. [10]
collected 4512 frames, including excavator, wheel loader, and worker,
by UAVs and proposed a YOLO-v3-based automated proximity measurement technique.
Similarly, Bang et al. [
59
] offered a Mask R-CNN-based method to segment, predict, and
monitor the proximities of workers and heavy equipment.
In conclusion, the ongoing evolution of deep learning and large datasets is significantly
enhancing the efficiency of construction applications. The integration of UAVs and object
detection algorithms is paving the way for sophisticated applications that were once
deemed challenging. The remaining sections delve into details of proposed AIDCON
datasets, which helps researchers and practitioners develop more accurate and reliable
methods for tracking and analyzing construction equipment.
3. Materials and Methods
This section details the comprehensive methodologies used to develop and evaluate
the AIDCON dataset. We begin by describing the dataset’s development process and then
outline the performance evaluation strategy used to validate the effectiveness of the dataset
with various deep learning algorithms. This methodical documentation provides clarity on
the processes involved in creating and utilizing AIDCON.
3.1. Dataset Development
The development of an extensive and varied image dataset, AIDCON, spanned a
long period. Aerial images were gathered during the construction monitoring processes
and organized for use in this dataset creation. The dataset development procedure can be
categorized into four main parts: image collection, privacy protection, image segmentation,
and clustering (Figure 1).
Remote Sens. 2024,16, 3295 6 of 22
Agronomy 2024, 14, x FOR PEER REVIEW 6 of 22
Figure 1. Stages of the dataset development process. First, aerial images were captured from con-
struction sites. Then, to ensure privacy, sensitive details were blurred in the images. Next, construc-
tion machinery was segmented and annotated into categories by annotators manually. Finally, im-
ages were clustered based on similarity and proximity metrics to organize training and testing splits,
ensuring to prevent model’s memorization.
3.1.1. Image Collection
Over the eight years spanning from 2015 to 2023, aerial images were collected from
25 distinct locations within the borders of Türkiye, as illustrated in Figure 2. The altitudes
of the images range from 10 to 150 m. The earlier images were obtained for the purpose
of construction monitoring, productivity measurement, and quality control research stud-
ies for construction projects by the authors. A subset of this image archive, including con-
struction machinery, was identified and selected for the AIDCON dataset. In addition to
the existing images, supplementary images were collected to account for less frequently
encountered construction machinery.
Figure 2. Geographic distribution of data collection sites.
Four UAVs were employed to collect data, including DJI’s Phantom 4 RTK, Mavic
Pro, Mavic 2 Pro, and Yuneec’s H520, as shown in Figure 3. Each drone was distinguished
by a unique set of specifications, including camera resolution, sensor type, flight time, and
range (Table 2). The Phantom 4 RTK has a high-resolution camera and extended range,
which makes it ideal for capturing aerial images from a longer distance. On the other
hand, the Mavic Pro and Mavic 2 Pro were portable, easy to use, and capable of capturing
stable footage in flight. The Yuneec H520 has a six-rotor system, which provides excellent
stability in windy conditions. High-quality images were captured by these UAVs,
Figure 1. Stages of the dataset development process. First, aerial images were captured from
construction sites. Then, to ensure privacy, sensitive details were blurred in the images. Next,
construction machinery was segmented and annotated into categories by annotators manually.
Finally, images were clustered based on similarity and proximity metrics to organize training and
testing splits, ensuring to prevent model’s memorization.
3.1.1. Image Collection
Over the eight years spanning from 2015 to 2023, aerial images were collected from
25 distinct locations within the borders of Türkiye, as illustrated in Figure 2. The altitudes
of the images range from 10 to 150 m. The earlier images were obtained for the purpose of
construction monitoring, productivity measurement, and quality control research studies
for construction projects by the authors. A subset of this image archive, including con-
struction machinery, was identified and selected for the AIDCON dataset. In addition to
the existing images, supplementary images were collected to account for less frequently
encountered construction machinery.
Agronomy 2024, 14, x FOR PEER REVIEW 6 of 22
Figure 1. Stages of the dataset development process. First, aerial images were captured from con-
struction sites. Then, to ensure privacy, sensitive details were blurred in the images. Next, construc-
tion machinery was segmented and annotated into categories by annotators manually. Finally, im-
ages were clustered based on similarity and proximity metrics to organize training and testing splits,
ensuring to prevent model’s memorization.
3.1.1. Image Collection
Over the eight years spanning from 2015 to 2023, aerial images were collected from
25 distinct locations within the borders of Türkiye, as illustrated in Figure 2. The altitudes
of the images range from 10 to 150 m. The earlier images were obtained for the purpose
of construction monitoring, productivity measurement, and quality control research stud-
ies for construction projects by the authors. A subset of this image archive, including con-
struction machinery, was identified and selected for the AIDCON dataset. In addition to
the existing images, supplementary images were collected to account for less frequently
encountered construction machinery.
Figure 2. Geographic distribution of data collection sites.
Four UAVs were employed to collect data, including DJI’s Phantom 4 RTK, Mavic
Pro, Mavic 2 Pro, and Yuneec’s H520, as shown in Figure 3. Each drone was distinguished
by a unique set of specifications, including camera resolution, sensor type, flight time, and
range (Table 2). The Phantom 4 RTK has a high-resolution camera and extended range,
which makes it ideal for capturing aerial images from a longer distance. On the other
hand, the Mavic Pro and Mavic 2 Pro were portable, easy to use, and capable of capturing
stable footage in flight. The Yuneec H520 has a six-rotor system, which provides excellent
stability in windy conditions. High-quality images were captured by these UAVs,
Figure 2. Geographic distribution of data collection sites.
Four UAVs were employed to collect data, including DJI’s Phantom 4 RTK, Mavic Pro,
Mavic 2 Pro, and Yuneec’s H520, as shown in Figure 3. Each drone was distinguished by
a unique set of specifications, including camera resolution, sensor type, flight time, and
range (Table 2). The Phantom 4 RTK has a high-resolution camera and extended range,
which makes it ideal for capturing aerial images from a longer distance. On the other
hand, the Mavic Pro and Mavic 2 Pro were portable, easy to use, and capable of capturing
stable footage in flight. The Yuneec H520 has a six-rotor system, which provides excellent
Remote Sens. 2024,16, 3295 7 of 22
stability in windy conditions. High-quality images were captured by these UAVs, ensuring
coverage of different types of construction machines from the different construction sites.
Construction image datasets that include UAV imagery in the literature are low-altitude,
and most images were taken from the side view [
30
]. In contrast, we present top-view
images taken by UAVs with a camera angle between 60–90 degrees facing downwards.
Agronomy 2024, 14, x FOR PEER REVIEW 7 of 22
ensuring coverage of different types of construction machines from the different construc-
tion sites. Construction image datasets that include UAV imagery in the literature are low-
altitude, and most images were taken from the side view [30]. In contrast, we present top-
view images taken by UAVs with a camera angle between 60–90 degrees facing down-
wards.
Figure 3. Specific UAV models employed for collecting aerial images (Images retrieved from
[60,61]).
Table 2. UAV specifications; the key parameters of the UAVs used in the study.
Sensor P4 RTK Mavic Pro Mavic 2 Pro Yuneec H520 E90
Sensor 1” CMOS 1/2.3” CMOS 1” CMOS 1” CMOS
FOV 84° 78.8° 77° 91°
Resolution (H × V) 5472 × 3648 4000 × 3000 4000 × 3000 5472 x 3648
Flight Time (mins) 30 27 31 30
Weight (g) 1391 734 907 1945
Transmission Range (km) 7 7 10 7
Most of the locations from which data were gathered are construction job sites such
as excavation fields, steel structures, reinforced concrete structures, and transportation
projects (Figure 4a). Before being transported to the job sites, construction machines are
generally parked in the parking areas for not being assigned to job sites or maintenance
purposes. A diverse range of machines was gathered in the same place, which makes this
place a perfect spot for data collection. Therefore, UAV footage over parking areas was
deployed, and the AIDCON dataset was enlarged by taking parked construction ma-
chines (Figure 4b). The machines’ various poses and scales for construction job sites and
parking areas have been achieved. In addition, a small portion of images from construc-
tion job sites that do not include any construction machines were added to the image set
to represent negative samples (Figure 4c). Incorporating negative samples in the dataset
can help deep learning algorithms achieve higher accuracy by improving their ability to
distinguish between relevant and irrelevant patterns in the data and by increasing their
generalizability to new and unseen data.
During such a long data collection period, construction sites’ weather conditions
change from summer to winter (Figure 5). Therefore, the dataset was carefully curated to
include various images captured under different weather. Sunny and snowy images were
included in the dataset to enable the model to recognize construction machines under
varying environmental conditions. Additionally, all images were captured during day-
time hours to ensure consistency across the dataset. The image format selected for the
dataset was JPG, as it is a commonly used and widely accepted format. Furthermore,
WGS-84 coordinates, specifically the latitude and longitude of the UAV, were embedded
in the images’ metadata to facilitate their location identification, thereby enabling the
grouping of images taken on the same construction sites.
Figure 3. Specific UAV models employed for collecting aerial images (Images retrieved from [
60
,
61
]).
Table 2. UAV specifications; the key parameters of the UAVs used in the study.
Sensor P4 RTK Mavic Pro Mavic 2 Pro Yuneec H520 E90
Sensor 1” CMOS 1/2.3” CMOS 1” CMOS 1” CMOS
FOV 84◦78.8◦77◦91◦
Resolution (H ×V) 5472 ×3648 4000 ×3000 4000 ×3000 5472 ×3648
Flight Time (mins) 30 27 31 30
Weight (g) 1391 734 907 1945
Transmission Range (km) 7 7 10 7
Most of the locations from which data were gathered are construction job sites such
as excavation fields, steel structures, reinforced concrete structures, and transportation
projects (Figure 4a). Before being transported to the job sites, construction machines are
generally parked in the parking areas for not being assigned to job sites or maintenance
purposes. A diverse range of machines was gathered in the same place, which makes this
place a perfect spot for data collection. Therefore, UAV footage over parking areas was
deployed, and the AIDCON dataset was enlarged by taking parked construction machines
(Figure 4b). The machines’ various poses and scales for construction job sites and parking
areas have been achieved. In addition, a small portion of images from construction job sites
that do not include any construction machines were added to the image set to represent
negative samples (Figure 4c). Incorporating negative samples in the dataset can help deep
learning algorithms achieve higher accuracy by improving their ability to distinguish
between relevant and irrelevant patterns in the data and by increasing their generalizability
to new and unseen data.
During such a long data collection period, construction sites’ weather conditions
change from summer to winter (Figure 5). Therefore, the dataset was carefully curated
to include various images captured under different weather. Sunny and snowy images
were included in the dataset to enable the model to recognize construction machines under
varying environmental conditions. Additionally, all images were captured during daytime
hours to ensure consistency across the dataset. The image format selected for the dataset
was JPG, as it is a commonly used and widely accepted format. Furthermore, WGS-84
coordinates, specifically the latitude and longitude of the UAV, were embedded in the
images’ metadata to facilitate their location identification, thereby enabling the grouping of
images taken on the same construction sites.
Remote Sens. 2024,16, 3295 8 of 22
Agronomy 2024, 14, x FOR PEER REVIEW 8 of 22
Figure 4. Example images from the AIDCON dataset. (a) Images gathered from ongoing construc-
tion sites, (b) UAV footage of parking areas with parked construction machines, (c) Negative sam-
ples, images without machines.
Figure 5. Weather variations at the same construction site during data collection.
3.1.2. Privacy Protection
The privacy of individuals and corporate entities was considered during the dataset’s
development. Building windows and the faces of individuals who might have been cap-
tured looking up at the UAV camera were intentionally blurred to prevent potential
Figure 4. Example images from the AIDCON dataset. (a) Images gathered from ongoing construction
sites, (b) UAV footage of parking areas with parked construction machines, (c) Negative samples,
images without machines.
Agronomy 2024, 14, x FOR PEER REVIEW 8 of 22
Figure 4. Example images from the AIDCON dataset. (a) Images gathered from ongoing construc-
tion sites, (b) UAV footage of parking areas with parked construction machines, (c) Negative sam-
ples, images without machines.
Figure 5. Weather variations at the same construction site during data collection.
3.1.2. Privacy Protection
The privacy of individuals and corporate entities was considered during the dataset’s
development. Building windows and the faces of individuals who might have been cap-
tured looking up at the UAV camera were intentionally blurred to prevent potential
Figure 5. Weather variations at the same construction site during data collection.
3.1.2. Privacy Protection
The privacy of individuals and corporate entities was considered during the dataset’s
development. Building windows and the faces of individuals who might have been cap-
Remote Sens. 2024,16, 3295 9 of 22
tured looking up at the UAV camera were intentionally blurred to prevent potential privacy
concerns. Additionally, the names, logos, and slogans of companies visible in the images
were acknowledged and appropriately covered to avoid any unintended consequences of
privacy violations. This approach ensured that the dataset complied with ethical considera-
tions and was developed following best practices in data privacy (Figure 6).
Agronomy 2024, 14, x FOR PEER REVIEW 9 of 22
privacy concerns. Additionally, the names, logos, and slogans of companies visible in the
images were acknowledged and appropriately covered to avoid any unintended conse-
quences of privacy violations. This approach ensured that the dataset complied with eth-
ical considerations and was developed following best practices in data privacy (Figure 6).
Figure 6. Privacy protection techniques applied within the AIDCON dataset (The left image demon-
strates the blurring of building windows, the right image shows the masking of company names
and logos).
3.1.3. Image Segmentation
Considering the construction machine datasets in the literature and field expert opin-
ions, construction machine categories were selected. The chosen construction machine cat-
egories are dump truck, excavator, backhoe loader, wheel loader, compactor, dozer, and
grader (Figure 7). Machines with fewer occurrences, like asphalt paving machines and
drilling machines, were grouped and named the “other” category. Cars frequently ap-
peared inside or near the construction area in the aerial images; therefore, they were la-
beled and grouped separately.
In image segmentation and annotation, five experts were assembled to constitute the
team, and they were informed of the annotation methodology. The dataset was divided
into 100 image subsets and subsequently allocated to the annotators. A cloud-based image
annotation tool, CVAT [62], was utilized in this study. The CVAT application was de-
ployed on a web server within the Amazon Web Services infrastructure, enabling parallel
execution of the procedure through cloud-based services (Figure 8). In total, annotators
devoted 82.38 person-hours to the task of image segmentation and annotation. Since the
boundaries of instances were drawn rather than only bounding boxes, the annotation pro-
cedure is more labor-intensive than preparing only an object detection dataset. The image
dataset was composed in COCO data format and saved in JSON file format. Eventually,
9563 instances were segmented and labeled in 2155 images.
Figure 6. Privacy protection techniques applied within the AIDCON dataset (The left image demon-
strates the blurring of building windows, the right image shows the masking of company names
and logos).
3.1.3. Image Segmentation
Considering the construction machine datasets in the literature and field expert opin-
ions, construction machine categories were selected. The chosen construction machine
categories are dump truck, excavator, backhoe loader, wheel loader, compactor, dozer,
and grader (Figure 7). Machines with fewer occurrences, like asphalt paving machines
and drilling machines, were grouped and named the “other” category. Cars frequently
appeared inside or near the construction area in the aerial images; therefore, they were
labeled and grouped separately.
In image segmentation and annotation, five experts were assembled to constitute the
team, and they were informed of the annotation methodology. The dataset was divided
into 100 image subsets and subsequently allocated to the annotators. A cloud-based image
annotation tool, CVAT [
62
], was utilized in this study. The CVAT application was deployed
on a web server within the Amazon Web Services infrastructure, enabling parallel execution
of the procedure through cloud-based services (Figure 8). In total, annotators devoted
82.38 person-hours to the task of image segmentation and annotation. Since the boundaries
of instances were drawn rather than only bounding boxes, the annotation procedure is more
labor-intensive than preparing only an object detection dataset. The image dataset was
composed in COCO data format and saved in JSON file format. Eventually, 9563 instances
were segmented and labeled in 2155 images.
Remote Sens. 2024,16, 3295 10 of 22
Agronomy 2024, 14, x FOR PEER REVIEW 10 of 22
Figure 7. Examples of selected categories.
Figure 8. CVAT annotation interface (Red boxes illustrate: Selection tools, Image Navigation, Area
Selection, List of Annotated objects).
3.1.4. Clustering
When previous studies were reviewed, image shots of the same scenes were not
grouped separately and were placed in train-test splits. Similar, even duplicate images,
were found on the construction datasets in the literature. Because of the similar images,
although overall accuracy increases, overlapping images between training and test sets
cause memorization problems. The trained model should be tested with unseen images
during training. In the study of Xiao and Kang [29], the duplication removal procedure
Figure 7. Examples of selected categories.
Agronomy 2024, 14, x FOR PEER REVIEW 10 of 22
Figure 7. Examples of selected categories.
Figure 8. CVAT annotation interface (Red boxes illustrate: Selection tools, Image Navigation, Area
Selection, List of Annotated objects).
3.1.4. Clustering
When previous studies were reviewed, image shots of the same scenes were not
grouped separately and were placed in train-test splits. Similar, even duplicate images,
were found on the construction datasets in the literature. Because of the similar images,
although overall accuracy increases, overlapping images between training and test sets
cause memorization problems. The trained model should be tested with unseen images
during training. In the study of Xiao and Kang [29], the duplication removal procedure
Figure 8. CVAT annotation interface (Red boxes illustrate: Selection tools, Image Navigation, Area
Selection, List of Annotated objects).
3.1.4. Clustering
When previous studies were reviewed, image shots of the same scenes were not
grouped separately and were placed in train-test splits. Similar, even duplicate images,
were found on the construction datasets in the literature. Because of the similar images,
although overall accuracy increases, overlapping images between training and test sets
cause memorization problems. The trained model should be tested with unseen images
Remote Sens. 2024,16, 3295 11 of 22
during training. In the study of Xiao and Kang [
29
], the duplication removal procedure
was completed manually. They noted that the possibility of automating the removal of
duplicated images by utilizing algorithms that can measure image similarity would be
explored. In our proposed AIDCON dataset, there are also overlapping images. Although
view angles differ, some machines have included more than one image. Therefore, it is
necessary to identify and group them to organize training and test splits appropriately.
From this perspective, images were clustered using image similarity and proximity metrics.
Example images with similarity and proximity metrics are given in Figure 9.
Agronomy 2024, 14, x FOR PEER REVIEW 11 of 22
was completed manually. They noted that the possibility of automating the removal of
duplicated images by utilizing algorithms that can measure image similarity would be
explored. In our proposed AIDCON dataset, there are also overlapping images. Although
view angles differ, some machines have included more than one image. Therefore, it is
necessary to identify and group them to organize training and test splits appropriately.
From this perspective, images were clustered using image similarity and proximity met-
rics. Example images with similarity and proximity metrics are given in Figure 9.
Figure 9. Two examples of clustering demonstrating clustering metrics, similarity and proximity.
Similarity percentage and proximity metrics were computed to cluster similar im-
ages. A Sentence Transformers-based image similarity calculation approach was em-
ployed to calculate the similarity percentage [63]. This technique measures the similarity
between images by leveraging their textual descriptions. This approach converts each im-
age’s content into fixed-size vector representations, or sentence embeddings, using Sen-
tence Transformers. The resulting embeddings capture the semantic information of the
descriptions, allowing for the computation of similarity scores between pairs of images
using metrics such as cosine similarity or Euclidean distance. This method transforms vis-
ual comparison into semantic comparison, making it applicable for image clustering. Sec-
ondly, GPS coordinates extracted from images’ metadata were used to calculate distances
as a proximity calculation. Images with more than 95% similarities and closer by 500 m
were allocated in the same cluster, resulting in 1386 clusters. Cluster IDs were added to
the labeling JSON file. Considering these cluster IDs, the number of images that have
identical IDs was used in the same split, such as training or testing.
The annotation file format includes three main arrays: images, annotations, and cat-
egories. The images array contains objects with properties such as id, width, height,
file_name, hasCategories (an array of category IDs associated with the image), and clus-
terID (the cluster ID assigned based on similarity and proximity calculations). The anno-
tations array includes objects detailing the annotations for each image, with properties
Figure 9. Two examples of clustering demonstrating clustering metrics, similarity and proximity.
Similarity percentage and proximity metrics were computed to cluster similar images.
A Sentence Transformers-based image similarity calculation approach was employed to
calculate the similarity percentage [
63
]. This technique measures the similarity between
images by leveraging their textual descriptions. This approach converts each image’s
content into fixed-size vector representations, or sentence embeddings, using Sentence
Transformers. The resulting embeddings capture the semantic information of the descrip-
tions, allowing for the computation of similarity scores between pairs of images using
metrics such as cosine similarity or Euclidean distance. This method transforms visual
comparison into semantic comparison, making it applicable for image clustering. Secondly,
GPS coordinates extracted from images’ metadata were used to calculate distances as a
proximity calculation. Images with more than 95% similarities and closer by 500 m were
allocated in the same cluster, resulting in 1386 clusters. Cluster IDs were added to the
labeling JSON file. Considering these cluster IDs, the number of images that have identical
IDs was used in the same split, such as training or testing.
Remote Sens. 2024,16, 3295 12 of 22
The annotation file format includes three main arrays: images, annotations, and
categories. The images array contains objects with properties such as id, width, height,
file_name, hasCategories (an array of category IDs associated with the image), and clusterID
(the cluster ID assigned based on similarity and proximity calculations). The annotations
array includes objects detailing the annotations for each image, with properties like id,
image_id (the ID of the image to which this annotation belongs), category_id (indicating
what is depicted), segmentation (coordinates defining the segmentation mask), and bbox
(bounding box coordinates). The categories array lists the categories with properties such
as id and name. This structured format facilitates efficient clustering with cluster IDs
organizing images into groups based on similarity scores and proximity metrics, aiding in
dataset splitting for training and testing purposes. Metadata such as altitude and sensor
information are also available in the images’ EXIF data. Table 3shows an example of
elements of JSON file format.
Table 3. Annotation file format.
Images Annotations Categories
. . .
{
“id”: 697,
“width”: 5472,
“height”: 3648,
“file_name”: “images05”.jpg”,
“hasCategories”: [
1,
1,
7,
3,
2,
5,
4,
5
],
“clusterID”: 5
}
. . .
. . .
{
“id”: 3874,
“image_id”: 697,
“category_id”: 7,
“segmentation”: [
[
493.05,
. . .
2128.57
]
],
“bbox”: [
87.72,
1949.66,
412.43,
869.26
]
}
. . .
. . .
{
“id”: 2,
“name”: “excavator”
},
{
“id”: 3,
“name”: “backhoe_loader”
},
{
“id”: 4,
“name”: “wheel_loader”
},
{
“id”: 5,
“name”: “compactor “
}
. . .
The graphs given below outline the characteristics of the AIDCON dataset. Figure 10
shows the number of objects and the number of images for each type of construction
machine. Excavators, dump trucks, cars, and backhoe loaders have the most instances
per image. Figure 11 shows the number of objects per image. A total of 254 negative
samples were added to the dataset. Thus, 11.79% of images do not include any objects
of construction machines. Compared to previously presented ground-level construction
datasets, the proposed dataset has more objects per image due to the broader area of the
UAV’s wide-angle camera. Figure 12 demonstrates the number of categories per image,
and Figure 13 shows the distribution of the bounding box size containing objects. Most
objects are small because of the high altitude of most UAV footage.
Remote Sens. 2024,16, 3295 13 of 22
Agronomy 2024, 14, x FOR PEER REVIEW 13 of 22
Figure 10. Number of objects and number of images for each type of construction machine.
Figure 11. Number of objects per image (%).
Figure 12. Number of categories per image (%).
Figure 10. Number of objects and number of images for each type of construction machine.
Agronomy 2024, 14, x FOR PEER REVIEW 13 of 22
Figure 10. Number of objects and number of images for each type of construction machine.
Figure 11. Number of objects per image (%).
Figure 12. Number of categories per image (%).
Figure 11. Number of objects per image (%).
Agronomy 2024, 14, x FOR PEER REVIEW 13 of 22
Figure 10. Number of objects and number of images for each type of construction machine.
Figure 11. Number of objects per image (%).
Figure 12. Number of categories per image (%).
Figure 12. Number of categories per image (%).
Remote Sens. 2024,16, 3295 14 of 22
Agronomy 2024, 14, x FOR PEER REVIEW 14 of 22
Figure 13. Distribution of the object size (%).
3.2. Performance Evaluation
Evaluating the performance of a dataset is essential to ensure the effectiveness and
reliability of machine learning models. By understanding the dataset’s strengths and
weaknesses, researchers can make decisions on its suitability for a specific application. For
the proposed AIDCON dataset, we performed a performance analysis using DL algo-
rithms and presented the results in the following parts.
Performance analysis was performed using five different DL algorithms commonly
used in computer vision-based research. The detectors were primarily selected from two-
stage algorithms since they have better accuracy rates than one-stage algorithms [64,65].
Although one-stage algorithms performed much faster results in the construction da-
tasets, we do not aim to create real-time applications [29,30].
The selected algorithms are Mask R-CNN [16], Cascade Mask R-CNN [17], Mask
Scoring R-CNN [18], Hybrid Task Cascade [19] and Pointrend [20]. The image sizes were
downsampled into 1300 × 500 pixels to optimize computational resources. ResNet50+FPN
was chosen as the backbone for all algorithms. The algorithms were trained for a total of
20 epochs, with an initial learning rate of 0.02. To improve convergence and reduce over-
fitting, the learning rate was reduced by a factor of 0.1 at the end of the 16th and 19th
epochs; a common technique is known as the learning rate schedule.
Two training rounds were conducted to examine the impact of clustering similar im-
ages in the same splits—one utilizing a clustering strategy and the other without cluster-
ing. The dataset was split into 80% training and 20% testing images. COCO dataset metrics
[6] were used for the evaluation of the performance of the AIDCON dataset. The primary
metric is mean average precision (mAP), which depends on precision and recall calcula-
tions defined below.
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃
𝑇𝑃 + 𝐹𝑃 (1)
𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃
𝑇𝑃 + 𝐹𝑁 (2)
𝐴
𝑃= 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛
(3)
In Equations (1) and (2), true positives (TP) denote correct detections, false positives
(FP) are the instances belonging to negative classes but labeled as positive, and false neg-
atives (FN) are the ones that belong to positive classes but are labeled as negative ones.
Correct detection is determined if the intersection over union (IoU) is above 50%, in which
Figure 13. Distribution of the object size (%).
3.2. Performance Evaluation
Evaluating the performance of a dataset is essential to ensure the effectiveness and
reliability of machine learning models. By understanding the dataset’s strengths and
weaknesses, researchers can make decisions on its suitability for a specific application. For
the proposed AIDCON dataset, we performed a performance analysis using DL algorithms
and presented the results in the following parts.
Performance analysis was performed using five different DL algorithms commonly
used in computer vision-based research. The detectors were primarily selected from two-
stage algorithms since they have better accuracy rates than one-stage algorithms [
64
,
65
].
Although one-stage algorithms performed much faster results in the construction datasets,
we do not aim to create real-time applications [29,30].
The selected algorithms are Mask R-CNN [
16
], Cascade Mask R-CNN [
17
], Mask
Scoring R-CNN [
18
], Hybrid Task Cascade [
19
] and Pointrend [
20
]. The image sizes were
downsampled into 1300
×
500 pixels to optimize computational resources. ResNet50+FPN
was chosen as the backbone for all algorithms. The algorithms were trained for a total
of 20 epochs, with an initial learning rate of 0.02. To improve convergence and reduce
overfitting, the learning rate was reduced by a factor of 0.1 at the end of the 16th and 19th
epochs; a common technique is known as the learning rate schedule.
Two training rounds were conducted to examine the impact of clustering similar im-
ages in the same splits—one utilizing a clustering strategy and the other without clustering.
The dataset was split into 80% training and 20% testing images. COCO dataset metrics [
6
]
were used for the evaluation of the performance of the AIDCON dataset. The primary
metric is mean average precision (mAP), which depends on precision and recall calculations
defined below.
Precision =TP
TP +FP (1)
Recall =T P
TP +F N (2)
AP =∑
Recall
Precision (3)
In Equations (1) and (2), true positives (TP) denote correct detections, false positives
(FP) are the instances belonging to negative classes but labeled as positive, and false
negatives (FN) are the ones that belong to positive classes but are labeled as negative ones.
Correct detection is determined if the intersection over union (IoU) is above 50%, in which
IoU is the ratio of the predicted segmentation mask with the ground truth mask. Average
precision (AP) is calculated by measuring average precision via different recall levels for
each class (Equation (3)).
Remote Sens. 2024,16, 3295 15 of 22
The mAP is the mean of all APs of all classes. mAPs of different IoU threshold levels
of each algorithm are presented in Tables 4and 5. The mAP metric is the average of all
APs for IoU, which ranges from 50% to 95%, with a step size of 5%. mAP
50
and mAP
75
denote IoU 50% and 75% threshold level mAPs, respectively. mAP
s
, mAP
m
, and mAP
l
are the mean average precision of small, medium, and large-sized objects. Small objects’
area is less than 32
2
pixels, medium objects’ area is between 32
2
and 96
2
pixels, and large
objects’ area is greater than 96
2
pixels. The mAP
s
is not presented in the tables since no
objects within the dataset have an area smaller than 32 pixels.
Table 4. Results of the clustered data (mAP
m
and mAP
l
are the mAP of medium- and large-sized objects).
Algorithm mAP mAP50 mAP75 mAPmmAPl
Hybrid Task Cascade 67.7 92.4 81.5 47.1 69.0
Cascade Mask R-CNN
66.2 91.0 81.4 52.4 67.6
Mask Scoring R-CNN
66.1 88.4 80.0 36.6 68.0
Pointrend 68.2 92.6 83.5 49.7 69.6
Mask R-CNN 66.6 91.6 80.4 46.8 67.8
Table 5. Results of the unclustered data (mAP
m
and mAP
l
are the mAP of medium- and large-
sized objects).
Algorithm mAP mAP50 mAP75 mAPmmAPl
Hybrid Task Cascade 71.9 93.7 86.4 60.4 72.5
Cascade Mask R-CNN
70.7 93.4 85.0 58.9 71.4
Mask Scoring R-CNN
72.9 93.7 88.3 41.9 73.8
Pointrend 72.9 94.2 88.4 49.2 73.6
Mask R-CNN 71.4 93.9 86.6 57.1 72.3
The training was performed on a computer with Intel(R) Core(TM) i7-10700K CPU
@ 3.0 GHz with 12 cores, 128 GB memory, and an NVIDIA GeForce RTX 2080 Ti graphics
card with Ubuntu 18.04 operating system. Model training was completed via open source
object detection toolbox MMDetection 2.18.1, based on the PyTorch framework [66].
The classwise AP of the best-performed DL algorithm is tabulated in Table 6, in which
the IoU threshold is selected at 50%. All IoUs are calculated considering segmentation
masks rather than bounding boxes.
Table 6. Classwise AP Results (IoU = 50%) (D.T: Dump Truck, Exc: Excavator, B.L.: Backhole Loader,
W.L.: Wheel Loader, Com.: Compactor, Doz.: Dozer, Gra.: Grader).
Algorithm D.T Exc. B.L. W.L. Com. Doz. Gra. Car Other
Pointrend
(clustered) 97.1 97.5 92.2 92.8 91.5 95.9 92.3 96.5 77.7
Pointrend
(unclustered) 97.3 97.8 97.9 96.6 92.6 86.5 100 95 84.2
4. Results and Discussion
Current construction-related datasets have images taken using fixed or mobile cameras
taken on a ground level. Although many applications are based on UAV imagery, there is
no large-scale and open aerial image set. This study’s main contribution to the literature is
the presentation of an open on-site image dataset, including aerial views of construction
machines annotated at the pixel level. Example detections of deep learning algorithms
Remote Sens. 2024,16, 3295 17 of 22
Tables 4and 5present the mean average precision (mAP) values of five object segmen-
tation algorithms: Hybrid Task Cascade, Cascade Mask R-CNN, Mask Scoring R-CNN,
Pointrend, and Mask R-CNN. These algorithms are evaluated on a clustered and unclus-
tered dataset. The evaluation metrics used are mAP, mAP
50
, mAP
75
, mAP
m
, and mAP
l
.
These metrics assess the performance of the algorithms on different aspects, such as over-
all mAP, precision at different Intersections over Union (IoU) levels, and precision on
different-sized objects.
The results show that Pointrend performs the best in clustered and unclustered
datasets, achieving a maximum mAP
50
of 92.6% and 94.2%, respectively. The other four
algorithms’ mAP
50
range from 92.4% to 88.4% and 93.9% to 93.4% for clustered and un-
clustered datasets, respectively. The tables also show the mAP values for different IoU
threshold levels and medium- and large-sized objects. The results indicate that the proposed
AIDCON dataset performs relatively better detecting large objects than medium-sized ones.
Utilizing UAV-captured images offers several benefits over CCTV and mobile camera
imagery. The top-down view of construction sites using UAV-mounted high-resolution
cameras and GPS technology enables the accurate positioning of objects. UAVs provide
fast and safe imaging while minimizing occlusion problems when capturing ground-
level images.
The AIDCON dataset comprises images from various construction sites, such as
excavation fields, steel structures, reinforced concrete structures, and transportation projects.
By capturing images from diverse construction environments, the dataset provides a
comprehensive resource for researchers and professionals to develop and refine machine
learning algorithms and address construction management challenges across different
project types.
Segmenting construction machines in images offers several advantages over merely
detecting them, providing a comprehensive understanding of objects. By generating pixel-
wise masks for each construction machine, object segmentation allows for a more precise
representation of the machines’ boundaries, shapes, and orientations. Providing machine
layouts enables us to understand the poses and activities of machines. Object segmentation
makes it possible to analyze the spatial relationships between machines and workers. This
information can be leveraged to assess and optimize the use of machinery, identify potential
hazards or inefficiencies, and plan future activities more effectively. Moreover, segmenting
construction machines can aid in detecting anomalies, such as unauthorized access to
restricted areas or machinery operated in unsafe conditions. This level of automation can
enhance the site’s overall safety, reduce the reliance on manual inspections, and minimize
the risk of accidents.
The mean average precision (mAP) of a model trained on an unclustered set is higher
than that trained on a clustered set. This is because the unclustered data have utilized
similar images in both the training and testing phases, leading the model to memorize the
training data. Consequently, the accuracy of the testing set is optimistic and does not reflect
how well it will perform on new and unseen images. To avoid this problem, ensuring that
the training and testing sets are distinct and do not contain overlapping images is essential.
This study followed an image similarity and proximity-based clustering process, dividing
the dataset into multiple clusters. Each cluster is used for training and testing. This ensures
that the model is evaluated on data not seen during training. Therefore, compared to
the unclustered set, the results of the clustered set are more reliable and appropriate for
real-world applications.
The table of classwise AP results indicates that Pointrend achieves the highest AP for
most classes, including dump truck, excavator, backhoe loader, wheel loader, compactor,
dozer, grader, and car (Table 6). However, the algorithm encounters difficulty in accurately
identifying objects belonging to the “other” category, which comprises a diverse range of
classes such as mobile cranes, concrete pumpers, cement trucks, drilling machines, asphalt-
making machines, and forklifts, among others. This difficulty arises due to the limited
availability of images representing these classes, which were thus grouped for analysis.
Remote Sens. 2024,16, 3295 18 of 22
Given the varied shapes and features of the objects within the “other” category, learning
the distinctive characteristics of this class from a limited number of images presents a
considerable challenge. Consequently, there is a need for additional images to improve the
performance of the deep learning algorithms on this class of objects.
In construction sites, excavators and dump trucks generally work together to move
materials such as soil, rock, or debris. When excavators and dump trucks work closely
together, it can be challenging for algorithms to distinguish between them accurately.
One of the main issues that can arise is the merging of machine boundaries between the
excavator and dump truck at the top view, which can be seen in Figure 15. This can cause
confusion when trying to classify these machines separately, leading to erroneously labeling
them as one machine, and decreasing accuracy.
Agronomy 2024, 14, x FOR PEER REVIEW 18 of 22
The table of classwise AP results indicates that Pointrend achieves the highest AP for
most classes, including dump truck, excavator, backhoe loader, wheel loader, compactor,
dozer, grader, and car (Table 6). However, the algorithm encounters difficulty in accu-
rately identifying objects belonging to the “other” category, which comprises a diverse
range of classes such as mobile cranes, concrete pumpers, cement trucks, drilling ma-
chines, asphalt-making machines, and forklifts, among others. This difficulty arises due
to the limited availability of images representing these classes, which were thus grouped
for analysis. Given the varied shapes and features of the objects within the “other” cate-
gory, learning the distinctive characteristics of this class from a limited number of images
presents a considerable challenge. Consequently, there is a need for additional images to
improve the performance of the deep learning algorithms on this class of objects.
In construction sites, excavators and dump trucks generally work together to move
materials such as soil, rock, or debris. When excavators and dump trucks work closely
together, it can be challenging for algorithms to distinguish between them accurately. One
of the main issues that can arise is the merging of machine boundaries between the exca-
vator and dump truck at the top view, which can be seen in Figure 15. This can cause
confusion when trying to classify these machines separately, leading to erroneously label-
ing them as one machine, and decreasing accuracy.
Identifying thinner parts of machines, particularly those falling under the “other”
category, has been challenging. This issue is highlighted by the incomplete representation
of certain parts of a concrete pumper machine in Figure 15. To address this limitation,
capturing the varied poses of these machine classes is critical. This approach would pro-
vide a more comprehensive representation of the machines, facilitating more accurate
identification of their thinner components. Furthermore, when images are downsampled
to smaller sizes (in this case, 1300 × 500 pixels), the thinner parts of the objects may lose
essential details or become indistinct. This loss of information can make it difficult for the
algorithms to identify and segment those parts accurately.
Figure 15. Examples of misclassified machinery.
As indicated in the evaluation results, there are no objects within the dataset with an
area smaller than 322 pixels. This lack of small objects can be a problem when detecting
construction machines that appear smaller in aerial images due to factors such as the alti-
tude of UAV footage. Limiting the flight altitude to a maximum of 150 m is recommended
to ensure optimal performance.
The number of images in the AIDCON dataset is crucial for enhancing the perfor-
mance of machine learning models. By increasing the number of images, the dataset can
better represent the variability and complexity of construction machines, leading to
Figure 15. Examples of misclassified machinery.
Identifying thinner parts of machines, particularly those falling under the “other”
category, has been challenging. This issue is highlighted by the incomplete representation
of certain parts of a concrete pumper machine in Figure 15. To address this limitation,
capturing the varied poses of these machine classes is critical. This approach would
provide a more comprehensive representation of the machines, facilitating more accurate
identification of their thinner components. Furthermore, when images are downsampled
to smaller sizes (in this case, 1300
×
500 pixels), the thinner parts of the objects may lose
essential details or become indistinct. This loss of information can make it difficult for the
algorithms to identify and segment those parts accurately.
As indicated in the evaluation results, there are no objects within the dataset with an
area smaller than 32
2
pixels. This lack of small objects can be a problem when detecting
construction machines that appear smaller in aerial images due to factors such as the alti-
tude of UAV footage. Limiting the flight altitude to a maximum of 150 m is recommended
to ensure optimal performance.
The number of images in the AIDCON dataset is crucial for enhancing the perfor-
mance of machine learning models. By increasing the number of images, the dataset can
better represent the variability and complexity of construction machines, leading to im-
proved model generalizability and performance on unseen data. To address the limitations
observed in the current dataset, increasing the dataset size with diverse and representa-
tive images, including varied poses and machine types, is recommended to ensure more
accurate detection and classification.
Remote Sens. 2024,16, 3295 19 of 22
5. Conclusions
In conclusion, this paper presents the AIDCON dataset, an open, on-site image dataset
containing 2155 aerial images of 9563 construction machines annotated at the pixel level
belonging to 9 categories. The dataset enables researchers and professionals to develop
and refine machine learning algorithms for construction site management applications
across different project types, such as steel and reinforced concrete structures and trans-
portation projects.
The main contributions of this study can be summarized as follows: (i) An aerial
construction image dataset is proposed, which captures a wide variety of construction envi-
ronments and machinery types. It provides a valuable resource for researchers leveraging
aerial imagery for construction site analysis. (ii) The images in the dataset are labeled at the
pixel level, which enables an understanding of the poses and activities of machines. (iii) A
clustering strategy is introduced in this study. This strategy helps to create appropriate
training and testing splits and ensures reliable performance for real-world applications.
(iv) Finally, this study evaluates five state-of-the-art deep learning algorithms on the aerial
construction image dataset. The results provide insights into their performance and areas
for improvement, which can guide future research in this domain.
Challenges remain in accurately identifying objects in the “other” category and distin-
guishing between machines working in close proximity. To address these limitations and
improve the performance of deep learning algorithms on the AIDCON dataset, increas-
ing the dataset size with diverse and representative images, including varied poses and
machine types, is recommended.
The AIDCON dataset offers a valuable resource for advancing machine learning
algorithms in the construction industry, with the potential to transform site management,
improve safety, decrease dependency on manual inspections, and reduce accident risks. As
computer vision technology progresses, we expect the AIDCON dataset to underpin future
breakthroughs in monitoring and managing construction sites.
Author Contributions: Data curation, A.B.E.; methodology, A.B.E. and E.A.; writing—Original draft
preparation, A.B.E. and O.P.; writing—review and editing E.A.; project administration, O.P.; All
authors have read and agreed to the published version of the manuscript.
Funding: This research is funded by a grant from the Scientific and Technological Research Council
of Türkiye (TUBITAK), Grant No. 122M055. TUBITAK’s support is gratefully acknowledged.
Data Availability Statement: Upon request, the corresponding author of this study is willing to
provide some or all of the data gathered during the research. These materials can be accessible
through the website http://ai2lab.org/aidcon (accessed on 27 June 224).
Conflicts of Interest: The authors declare no conflicts of interest.
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