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DOI: 10.32604/cmc.2024.055049
ARTICLE
Efficient and Cost-Effective Vehicle Detection in Foggy Weather
for Edge/Fog-Enabled Traffic Surveillance and Collision Avoidance Systems
Naeem Raza1, Muhammad Asif Habib1, Mudassar Ahmad1,QaisarAbbas
2,*,MutlaqB.Aldajani
2and
Muhammad Ahsan Latif3
1Department of Computer Science, National Textile University, Faisalabad, 37610, Pakistan
2College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU),
Riyadh, 11432, Saudi Arabia
3Department of Computer Science, University of Agriculture, Faisalabad, 38000, Pakistan
*Corresponding Author: Qaisar Abbas. Email: qaabbas@imamu.edu.sa
Received: 14 June 2024 Accepted: 02 September 2024 Published: 15 October 2024
ABSTRACT
Vision-based vehicle detection in adverse weather conditions such as fog, haze, and mist is a challenging research
area in the fields of autonomous vehicles, collision avoidance, and Internet of Things (IoT)-enabled edge/fog
computing traffic surveillance and monitoring systems. Efficient and cost-effective vehicle detection at high
accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time. To evaluate
vision-based vehicle detection performance in foggy weather conditions, state-of-the-art Vehicle Detection in
Adverse Weather Nature (DAWN) and Foggy Driving (FD) datasets are self-annotated using the YOLO LABEL
tool and customized to four vehicle detection classes: cars, buses, motorcycles, and trucks. The state-of-the-art
single-stage deep learning algorithms YOLO-V5, and YOLO-V8 are considered for the task of vehicle detection.
Furthermore, YOLO-V5s is enhanced by introducing attention modules Convolutional Block Attention Module
(CBAM), Normalized-based Attention Module (NAM), and Simple Attention Module (SimAM) after the SPPF
module as well as YOLO-V5l with BiFPN. Their vehicle detection accuracy parameters and running speed is
validated on cloud (Google Colab) and edge (local) systems. The mAP50 score of YOLO-V5n is 72.60%, YOLO-
V5s is 75.20%, YOLO-V5m is 73.40%, and YOLO-V5l is 77.30%; and YOLO-V8n is 60.20%, YOLO-V8s is 73.50%,
YOLO-V8m is 73.80%, and YOLO-V8l is 72.60% on DAWN dataset. The mAP50 score of YOLO-V5n is 43.90%,
YOLO-V5s is 40.10%, YOLO-V5m is 49.70%, and YOLO-V5l is 57.30%; and YOLO-V8n is 41.60%, YOLO-V8s is
46.90%, YOLO-V8m is 42.90%, and YOLO-V8l is 44.80% on FD dataset. The vehicle detection speed of YOLO-
V5n is 59 Frame Per Seconds (FPS), YOLO-V5s is 47 FPS, YOLO-V5m is 38 FPS, and YOLO-V5l is 30 FPS; and
YOLO-V8n is 185 FPS, YOLO-V8s is 109 FPS, YOLO-V8m is 72 FPS, and YOLO-V8l is 63 FPS on DAWN dataset.
The vehicle detection speed of YOLO-V5n is 26 FPS, YOLO-V5s is 24 FPS, YOLO-V5m is 22 FPS, and YOLO-V5l
is 17 FPS; and YOLO-V8n is 313 FPS, YOLO-V8s is 182 FPS, YOLO-V8m is 99 FPS, and YOLO-V8l is 60 FPS on
FD dataset. YOLO-V5s, YOLO-V5s variants and YOLO-V5l_BiFPN, and YOLO-V8 algorithms are efficient and
cost-effective solution for real-time vision-based vehicle detection in foggy weather.
KEYWORDS
Vehicle detection; YOLO-V5; YOLO-V5s variants; YOLO-V8; DAWN dataset; foggy driving dataset; IoT;
cloud/edge/fog computing
912 CMC, 2024, vol.81, no.1
1Introduction
Road traffic collisions are the primary cause of injuries, specifically for persons aged 5–29, with
approximately 1.3 million people dying a year. About 93% of the world’s road traffic fatalities occur
in low- to middle-income countries, which have about 60% of the world’s vehicles and cost 3% of most
countries’ gross domestic product [1]. According to the Pakistan bureau of statistics traffic accidents
annual report for 2022, in 2020–2021, the total number of reported traffic accidents was 10,429, with
4721 cases of fatal accidents and 5708 cases of non-fatal accidents. In total, 5816 people were killed,
12,886 were injured, and 15,351 vehicles were affected [2]. The number of reported road and traffic
accidents is still increasing, causing fatal and non-fatal injuries and deaths, road and infrastructure
damage, increasing traffic congestion on the roads, individual’s financial, health, and life loss, putting
the load on hospitals, etc. Inclement weather is the leading cause of road fatalities among traffic
crashes. According to ten years of statistics from the national highway traffic safety administration,
21% of vehicle crashes, 19% of crash injuries, and 16% of crash fatalities are due to inclement
or adverse weather conditions [3]. Adverse weather conditions such as fog, snow, rain, hail, slush,
ground blizzards, and strong winds negatively affect driving behavior, visibility distance, traffic f low
characteristics, vehicle performance, surface conditions, and road safety. Fog, fog condition, foggy
weather, or foggy area will result in the reduction of the road visibility distance, which is an alarming
situation because it distracts the normal driving behavior (car following, speed adjustments, and lane
changing) and reduces the efficiency of the traffic operations, hence resulting in road traffic crashes
[4]. State-of-the-art vehicle detection systems are the key solution to modern traffic surveillance, driver
assistance, and automated driving systems [5,6]. Object detection is a critical area of research in
computer vision. Object detection algorithms use camera sensors to locate and classify objects in
indoor and outdoor environments. Cameras are now more accurate at detecting objects and more
cost-effective than other sensors [7]. Rectangular bounding boxes locate the detected objects and
further classify or categorize them. Object detection for any real-time application involves object
classification, semantic, and instance segmentation [8]. IoT enabled smart transportation systems
and vehicle detection techniques are the essential technologies for traffic efficiency, optimization,
and surveillance [9,10]. Vehicle detection characteristics, challenges, algorithms, and applications are
presented in Fig. 1.
Figure 1: Vehicle detection characteristics, challenges, algorithms, and applications
CMC, 2024, vol.81, no.1 913
Generally, object detection consists of the localization of the object instances in an image
as well as the classification of the object into multiple categories by applying methods such as
Histogram of Oriented Gradient (HOG), scale invariant feature transform, spatial pyramid matching,
deformable part model, and selective search [11]. Single-stage object detection algorithms are also
known as non-region proposal or dense prediction algorithms. These algorithms are based on
single simpler network architecture for feature extraction, feature fusion, and detection. The famous
Single-stage object detection algorithms are single shot detector, You Only Look Once (YOLO)
and its versions (YOLO-V1-YOLO-V8), fully convolutional one-stage, EfficientDet, and detection
transformer algorithms. YOLO has emerged as a key object detection model, excelling in real-time
environments with remarkable accuracy and speed. Traditional region-based detection models often
struggled with speed because they required multiple passes over an image to identify objects. These
models are computationally demanding, limiting their use in real-time applications. In contrast,
YOLO’s one-shot detection approach greatly increased the speed of object detection. By dividing
the entire image into a grid and making predictions directly within each grid cell, YOLO achieved
impressive real-time processing, making it especially beneficial for applications that need quick and
accurate object identification in practical and dynamic environment [12]. Single-stage object detection
algorithms, more specifically YOLO-V5 and YOLO-V8 supports realtime speed of object detection
with reasonable accuracy. Two-stage object detection algorithms are also known as region proposal or
sparce prediction algorithms. These algorithms are based on region proposal network to extract the
region of interest for better feature extraction, feature fusion, and finally the classification/detection
network. The famous Two-stage object detection algorithms are Region-based CNN (R-CNN), fast
R-CNN, faster R-CNN, spatial pyramid pooling networks, and feature pyramid network. Two-stage
object detection algorithms supports higher accuracy with reasonable realtime detection speed as
compared to YOLO-V5 and YOLO-V8 algorithms [12–14]. Attention Mechanism (AM) of deep CNN
are inspired by the human visual system. The human visual system helps humans to efficiently and
effectively analyze and to focus on the most important parts of the normal to complex scenes in
real-time. In deep CNNs, attention mechanism is treated as the dynamic weight assignment process.
The AMs adaptively select and assign weights to the features of the input image in such a way
that, the important regions got best suitable weights [15,16]. In YOLO, attention mechanisms can
enhance the model’s focus on specific areas of an image that are crucial for object detection. Incorpo-
rating attention mechanisms in YOLO can improve the model’s accuracy and speed by concentrating
on the most relevant areas of the image [17]. Convolutional Block Attention Module (CBAM) [18],
Normalized-based Attention Module (NAM) [19], and Simple Attention Module (SimAM) [20]are
the most prominent AM techniques used to improve object detection algorithms performance. A
weighted Bi-directional Feature Pyramid Network (BiFPN) [21], which allows easy and fast multi-
scale feature fusion is also considered with YOLO-V5l.
Main Contributions
1. Vehicle detection methodology is proposed to evaluate the efficiency and cost-effectiveness of
vehicle detection on cloud and edge in foggy weather.
2. Vehicle detection is achieved by utilizing the YOLO-V5 and YOLO-V8 state-of-the-art deep
learning algorithms with Intersection Over Union (IOU) and Complete IOU (CIOU) loss
functions, and post-processing algorithm Non-Maximum Suppression (NMS). Furthermore,
YOLO-V5s variants (YOLO-V5s_CBAM, YOLO-V5s_NAM, YOLO-V5s_SimAM) and
YOLO-V5l_BiFPN are also proposed and evaluated.
3. Vehicle detection performance is evaluated on the DAWN and FD foggy weather vehicle
detection datasets. DAWN and FD datasets are pre-processed, customized, and self-annotated
to four vehicle detection classes: cars, buses, trucks, and motorcycles.
914 CMC, 2024, vol.81, no.1
4. Several state-of-the-art vehicle detection performance metrics are utilized, such as precision,
recall, precision-recall curve, F1 score, mean average precision, per-processing time, inference
time, and post-processing NMS time in terms of efficiency and effectiveness of the solution.
5. YOLO-V5, YOLO-V5s variants, and YOLO-V8 vehicle detection system empowers IoT
edge/fog and cloud enabled traffic surveillance and collision avoidance.
The paper’s overall organization is given as follows. The literature review section presented the
state-of-the-art literature in the field of object detection and vehicle detection more specifically using
YOLO series of algorithms, and state-of-the-art related work. The step-by-step vehicle detection
methodology section provides the data collection, pre-processing, self-annotation, customization of
DAWN and FD datasets, experimental parameters, and evaluation metrics setup for training and
validation of YOLO-V5 and YOLO-V8 models. The results section presents the vehicle detection
results of YOLO-V5, YOLO-V5s variants, YOLO-V5l_BiFPN, and YOLO-V8 models on the DAWN
and FD datasets. The comparative analysis and discussion section, presents the vehicle detection
performance of YOLO-V5, YOLO-V5s variants, YOLO-V5l_BiFPN, and YOLO-V8 algorithms in
terms of accuracy and speed on DAWN and FD datasets. Lastly, the conclusion and future work
section summarizes the current working and future perspective of the presented research work.
2Literature Review
Computer vision based deep learning algorithms and IoT technologies enables real-time video
processing and analytics for smart cities [22]. The structure of deep learning algorithms regarding
the backbone and other network layers is essential in speed and accuracy tradeoffs. So, an efficient
and lightweight vehicle detection network will be the ultimate goal of modern deep learning-based
CNNs. Single-stage object detection algorithms are faster for Frame Per Second (FPS) to meet real-
time vehicle detection requirements than two-stage algorithms. Still, they only face the challenge of
lower accuracy [23]. You Only Look Once (YOLO) is a state-of-the-art, single-stage deep learning
algorithm [24]. YOLO uses a deep neural network architecture to generate or predict object bounding
boxes and class-level probabilities of objects in a single pass directly from the input images. YOLO
detects and classifies objects by utilizing classification as a regression problem. The YOLO algorithm
divides the input images into grid cells, predicting class-level probabilities, confidence scores, bounding
boxes, and detecting objects. The YOLO version 5 (YOLO-V5) and YOLO version 8 (YOLO-V8)
are proposed by ultrasonics. The YOLO-V5 backbone network is based on Cross Stage Partial
(CSP) darknet and YOLO-V8 is based on improved CSP darknet. According to the overall network
architecture point of view, the main difference between YOLO-V5 and YOLO-V8 is the use of CSP
bottleneck convolutional layers. The YOLO-V5 uses C3 module that is based on CSP bottleneck
with 3 convolutional layers, whereas YOLO-V8 uses C2F module that is the faster implementation of
CSP bottleneck with 2 convolutional layers. The YOLO-V5 neck uses path aggregation network and
YOLO-V8 neck uses improved path aggregation network. YOLO-V5 uses feature pyramid network
and YOLO-V8 uses improved FPN. The YOLO-V5 is based on leaky ReLU activation function and
YOLO-V8 uses Sigmoid Linear Unit (SiLU) activation function. YOLO-V5 uses CIOU loss function,
whereas YOLO-V8 additionally uses distribution focal loss and variance focal loss functions. The more
advanced hyper-parameters and data augmentation techniques are available with YOLO-V8 algorithm
as compared to YOLO-V5 [25,26]. These algorithms offer better speed and improved accuracy than the
previous versions. These algorithms are classified into five models; nano, small, medium, large, and
extra-large. YOLO-V5 and YOLO-V8 nano to extra-large models have characteristics such a more
extensive network size, more network layers, more parameters, a more complex architecture, more
accuracy, and more processing time for inference.
CMC, 2024, vol.81, no.1 915
The performance evaluation was performed on the DAWN dataset using only the YOLO-V5n
model. They only focused on domain-shift analysis and the effect of transfer learning without
providing vehicle detection results and analysis [27]. Another study focused on performance evaluation
on Roboflow datasets using only the YOLO-V5s model. They evaluated vehicles, pedestrians, and
traffic sign detection [28]. Vehicle detection performance analysis of YOLO-V5 models (s, m, l) and
YOLOv7 models was performed. They evaluated it based on the DAWN and augmented DAWN
datasets [29]. Another study of vehicle detection based on YOLO-V4 on the DAWN dataset was
conducted to assess accuracy, and they highlighted a mAP of 81% [30]. Object detection-based
performance evaluation was performed on the RTTS dataset using YOLO-V5 in foggy weather. They
reported a mAP of 73.4% [31]. An optimized YOLO-V5 model was proposed and evaluated on the
DAWN and COCO datasets in adverse weather conditions [32]. The vehicle detection performance in
adverse weather conditions is evaluated by utilizing the Google Colab platform on ACDC, DAWN,
and merged datasets using an improved YOLO-V8 algorithm. They achieved mAP of 67.2% on
DAWN, 74.2% on ACDC, and 91% on merged datasets [33]. The vehicle detection performance is
evaluated on Canadian vehicle datasets dataset using YOLO-V8 algorithm. The YOLO-V8 achieved
mAP of 73.47% on CVD dataset [34]. Based on the current literature and related work review, YOLO-
V5 and YOLO-V8 algorithms are not well studied for vehicle detection based on the DAWN and FD
datasets in foggy weather. IoT enabled closed circuit television smart cameras based vehicle detection
performance of HOG, Viola-Jones, SSD, YOLO-V8, and R-CNN were evaluated and concluded that
YOLO-V8 is outperformed for cost-efficient [35]. Realtime video analytics through YOLO-V5/YOLO-
V8 and IoT are the promising area of research in Intelligent Transportation Systems [36].
3Materials and Methods
This research aims to evaluate the vehicle detection performance of state-of-the-art YOLO-V5
and YOLO-V8 algorithms [25,26] and YOLO-V5s variants in foggy weather conditions. The overall
research methodology is illustrated in Fig. 2.
State-of-the-art foggy weather datasets DAWN and FD are utilized for the performance eval-
uation of vehicle detection in foggy weather conditions. The DAWN and FD datasets are self-
annotated using the YOLO LABEL tool and customized to four vehicle detection classes: cars, buses,
motorcycles, and trucks. A single-stage DL algorithms, YOLO-V5 and YOLO-V8 are utilized for the
performance evaluation of vehicle detection. The four YOLO-V5 models, YOLO-V5n, YOLO-V5s,
YOLO-V5m, and YOLO-V5l; YOLO-V5s variant, and similarly, four YOLO-V8 models, YOLO-
V8n, YOLO-V8s, YOLO-V8m, and YOLO-V8l are custom-trained and evaluated regarding vehicle
detection accuracy and speed. In general, state-of-the-art YOLO-V5 and YOLO-V8 algorithms have
three main stages: the backbone, neck, and detection head. The backbone layer of YOLO-V5 is based
on convolutional layers, C3-Bottleneck layers, and the SPPF layer. Similarly, the backbone layer of
YOLO-V8 is based on convolutional layers, C2F-Bottleneck layers, and the SPPF layer. The SPPF
uses a convolutional layer followed by batch normalization and SiLU activation function, collectively
called the CBS module. The intermediate neck layer of YOLO-V5 and YOLO-V8 performs feature
fusion and aggregation with the help of bottom-up and top-down layers and paths. The final detection
layer performs object detections by utilizing bounding boxes, IOU, and CIOU thresholds, and NMS
techniques. The complete architectural diagram of the YOLO-V5 and YOLO-V8 algorithms are shown
in Fig. 3a,b. In this research work, YOLO-V5s variants YOLO-V5s_CBAM, YOLO-V5s_NAM,
YOLO-V5s_SimAM based on attention mechanism modules after the SPPF module in the backbone
network as well as YOLO-V5l_BiFPN are proposed. The SiLU activation function is used in all
YOLO-V5s variants including YOLO-V5s and CBAM, NAM, and SimAM attention mechanisms
916 CMC, 2024, vol.81, no.1
instead of ReLU activation function. For vehicle detection, the choice between ReLU and SiLU
depends on the specific context. SiLU, particularly when used with advanced YOLO models, has
been shown to perform better in challenging environments such as sandy weather, offering higher
mean average precision (mAP). While ReLU is still favored for its simplicity and efficiency in standard
conditions, SiLU ability to handle complex patterns makes it a better option for scenarios requiring
more precise and robust detection, especially in dynamic environments [29].
Figure 2: Research methodology
Figure 3: Architectural diagrams of (a) YOLO-V5 Network Architecture, (b) YOLO-V8 Network
Architecture
3.1 Distance Metrics and Loss Functions
The CNN object detection algorithm’s localization and classification entirely depend on the
characteristics of the loss function. The loss function supports object detection algorithms to reach
optimal performance regarding bounding box regression and classification accuracy. The loss function
CMC, 2024, vol.81, no.1 917
attempts to minimize the difference between the predicted and ground truth bounding boxes [37]. IOU
[38,39], commonly known as the Jaccard Index, is the most popular metric for evaluating the impact
of bounding box regression on object detection. IOU distance is defined and expressed in Eq. (1):
Intersection Over Union (IOU)=|Bp∩Bgt |
|Bp∪Bgt|(1)
where Bgt represents the ground truth bounding box with four components in terms of xgt and ygt for
xand yaxis, width wgt and height hgt of the ground truth bounding box such as Eq. (2):
Bgt =(xgt,ygt ,wgt,hgt )(2)
Similarly, Bprepresents the predicted bounding box with four components in terms of xpand yp
for xand yaxis, width wpand height hpof the ground truth bounding box such as Eq. (3):
Bp=(xp,yp,wp,hp)(3)
The IOU loss function is defined to improve the performance of IOU metric. It works well
when bounding boxes have some or completely overlapped area. It means that when |Bp∩Bgt|=
|Bp∪Bgt|,IOU =1. IOU performs worst for the non-overlapped areas. It means that when
|Bp∩Bgt|=0, IOU (Bp,Bgt )=0.
The IOU loss is defined as Eq. (4):
LIOU =1−IOU =1−|Bp∩Bgt|
|Bp∪Bgt|(4)
To improve object detection performance in complex cases, the CIOU loss function is proposed
[40]. The CIOU distance metric and loss function incorporate geometric factors such as overlapping
area S, distance D, and aspect ratio V of the predicted and ground truth bounding boxes. The CIOU
distance metric is defined and expressed in Eq. (5):
Complete Intersection Over Union (CIOU)=IOU −ρ2(bp,bgt )
c2−αV(5)
where the equation of αand Vare expressed as Eqs. (6) and (7), respectively:
α=⎧
⎨
⎩
0, if IOU <0.5
V
(1−IOU)+Vif IOU ≥0.5 (6)
V=4
π2arctan wgt
hgt −arctan wp
hp2
(7)
Now the CIOU loss function is defined as Eq. (8):
LCIOU =1−CIOU =1−IOU +ρ2(bp,bgt)
c2+αV(8)
3.2 Data Collection and Pre-Processing
Two state-of-the-art foggy image datasets, DAWN and FD, are collected to evaluate the vehicle
detection performance in foggy weather conditions. The dataset balancing process is achieved by
removing the negative/background as well as corrupt images and labels. Unreliable models that lead
to poor decisions can arise from incomplete, erroneous, or inappropriate training data. To ensure
918 CMC, 2024, vol.81, no.1
trustworthy AI applications, it is crucial to have high-quality training and testing data characterized
by accuracy, completeness, and consistency [41].
3.2.1 Driving in Adverse Weather Nature (DAWN) Dataset
The DAWN dataset consists of on-road captured images in the real world under adverse weather
circumstances [42,43]. The DAWN dataset focuses on various traffic environments (urban, highway,
and motorway) and traffic flows. The DAWN dataset comprised 1000 images from real-traffic
situations, categorized into four weather conditions: fog, snow, rain, and sandstorms. The DAWN
dataset contains 300 images for fog, haze, and mist, 200 images for rain, 204 images for snow, and 323
images for the sand to evaluate the performance of vehicle detection and classifications algorithms in
adverse weather conditions. The DAWN dataset contains 7845 Ground Truth (GT) bounding boxes
with 98.48% GT bounding boxes available for vehicles, 7.07% for pedestrians, and 1.36% for bicycles
and motorcycles. DAWN foggy images are used to train and validate the vehicle detection performance
using YOLO-V5 and YOLO-V8. These foggy images include foggy, hazy, and misty weather images.
The foggy images in the DAWN dataset are split into 80% for training and 20% for validation using
the holdout method. Furthermore, the DAWN dataset is customized to four vehicle detection classes:
cars, buses, trucks, and motorcycles.
3.2.2 Foggy Driving (FD) Dataset
The FD [44,45] dataset consists of 101 color images depicting real-world FD scenes. Fifty-one
images were captured with a cell phone camera in foggy conditions in various areas of Zurich, and
the rest of the 50 images were collected from the web. The maximum image resolution in the dataset
is 960 ×1280 pixels. The FD dataset originally comprised images and corresponding bounding box
labels with non-normalization for eight object classes, such as cars, pedestrians, riders, buses, trucks,
trains, motorcycles, and bicycles. To perform vehicle detection tasks using the YOLO-V5 and YOLO-
V8 algorithms, FD dataset images are self-annotated using the YOLO label [46] tool into four vehicle
detection classes, such as car, bus, truck, and motorcycle, in YOLO format with normalized values.
The whole process of FD dataset customization is depicted in Fig. 4. FD dataset images are split into
80% images for training images and 20% images as validation images using the holdout method.
3.3 Experimental Setup
The experimental setup is based on cloud (Google Colab) and edge (local) systems enabled with
GPU resources. The Google Colab cloud environment provides Invidia Tesla T4 GPU with 15 GB
RAM, system RAM of 12.7 GB, and disk storage of 78.2 GB with Google drive integration. The
local system resources are acer workstation with 64-bit Windows 11 operating system, 2.30 GHz
Intel Core i5 8th generation (8300H) CPU, 24 GB RAM, and Invidia GeForce GTX 1050 Ti GPU.
These systems are CUDA enabled with the integration of python, PyTorch, and Ultralytics along
with essential libraries and packages. The basic training and validation parameters for YOLO-V5 and
YOLO-V8 are tabulated in Table 1. Cloud GPUs offer significant computational power for training
complex models with large datasets, enhancing detection accuracy and scalability. They support
diverse datasets, improving model generalization, but may introduce latency data transmission and
incur high operational costs. In contrast, edge GPUs facilitate real-time processing by handling data
locally, which reduces latency and allows for quick decision-making. They decrease data transmission
needs and save bandwidth. However, edge GPUs typically have lower computational power, which can
limit model complexity and accuracy, though they provide flexible deployment options across various
environments [47].
CMC, 2024, vol.81, no.1 919
Figure 4: Foggy Driving (FD) dataset self-annotation and customization
Table 1: YOLO-V5, YOLO-V5s variants and YOLO-V8 experimental parameters
Parameters YOLO-V5, YOLO-V5s variants YOLO-V8
Models Nano, small, medium, large
Datasets DAWN, FD
Classes/Names Bus, car, motorcycle, truck
Image size 640
Batch-size 16
Epochs 300
Optimizer SDG AdamW
Hyper-parameters lr0, lrf =0.01, momentum =0.937, box =
0.05, cls =0.5, obj =1.0, iou_t =0.2
lr0, lrf =0.01, momentum =0.937,
box =7.5, cls =0.5, dfl =1.5
Auto anchors 5.46 anchors/target for DAWN, 4.87
anchors/target (FD)
Anchor-free
Anchors 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59,
119, 116, 90, 156, 198, 373, 326
Anchor-free
3.4 Performance Evaluation Metrics
The state-of-the-art precision, recall, Mean Average Precision (mAP), F1 score, and Frame Per
Seconds (FPS) [48] performance evaluation metrics are employed in this research work. The details
of these evaluation metrics are described below. YOLO emphasizes both accuracy and speed, and the
mAP metric reflects this by evaluating both aspects. mAP considers the model’s precision (the accuracy
of detections) and recall (the completeness of detections), providing a well-rounded assessment of
its performance. In YOLO versions, the F1 score is not used as a primary metric because, in object
detection tasks where some classes are much more common than others, the F1 score can be heavily
biased towards the majority class. This makes it less reliable for evaluating performance on minority
classes [12].
920 CMC, 2024, vol.81, no.1
1. Precision: It is the percentage accuracy of the object detection model to detect the right objects
correctly. It shows the positive predictive score of the model. It is the ratio between True Positive
(TP) predictions and True Positive (TP) plus True Negative (TN) predictions (overall positive
object classes predictions). The precision is expressed mathematically in Eq. (9).
Precision =TP
TP+FP
(9)
2. Recall: It is the percentage sensitivity or the probability of the object detection model for
detecting the ground truth objects. It is the ratio between True Positive (TP) predictions and
True Positive (TP) plus False Negative (FN) predictions (overall positive object instances
predictions). The recall is expressed mathematically in Eq. (10).
Recall =TP
TP+FN
(10)
3. Precision-Recall Curve: This curve is used to map the relationship between the precision and
recall scores of the object detector. The mapping of precision scores is on the Y-axis, and recall
is on the X-axis. Ideally, the ideal point lies near to (1.0, 1.0).
4. F1 Score: The Harmonic mean of precision and recall. A higher value means better classi-
fication accuracy of the model. The ability of an object detection algorithm to balance the
precision and recall scores. The F1 score is mathematically expressed in Eq. (11).
F1Score =2×Precision ×Recall
Precsion +Recall (11)
5. Average Precision (AP): It is the measure of the average precision for a set of 11 equally spaced
recall points (0, 0.1, 0.2, ..., 1). The relevant recall value of r corresponds to the precision value
and is interpolated for the maximum recall value of r greater than r. The AP is mathematically
expressed in Eq. (12).
AP =1
11 r=0,0.1,0.2,...1Pinterpolation(r)
Pinterpolation (r)=maxp (˜
r)
(˜
r)>r(12)
6. Mean Average Precision (mAP): It is the average of all the AP values for all N classes of objects
in a dataset. The higher the value, the better will be the object detection performance for all
object classes. The mAP is mathematically expressed in Eq. (13).
mAP =1
NN
i=1APi (13)
7. Frame Per Second (FPS): The number of frames processed by an object detection and tracking
algorithm per second [49]. FPS rate depends on pre-processing time, inference time, and post-
processing time.
4Results Gathering and Visualization
4.1 Performance Evaluation of Vehicle Detection Using YOLO-V5 and YOLO-V8 on the DAWN
Dataset
The YOLO-V5 and YOLO-V8 (nano, small, medium, large) models are custom-trained on DAWN
dataset for four vehicle detection classes (car, bus, truck, and motorcycle) with the selected model
CMC, 2024, vol.81, no.1 921
and training parameters, as highlighted in Table 1. The vehicle detection accuracy of YOLO-V5n in
terms of mAP@50 of car detection is 83.80 %, bus detection is 35.40%, truck detection is 71.80%, and
motorcycle detection is 99.50%. The overall mAP@50 is 72.60% for all vehicle detection classes. The
F1 score is 71%, with a confidence score of 50.70%. The vehicle detection accuracy of YOLO-v8n in
terms of mAP@50 of car detection is 85.60%, bus detection is 34.10%, truck detection is 70.80%, and
motorcycle detection is 50.10%. The overall mAP@50 is 60.20% for all vehicle detection classes. The
F1 score is 62%, with a confidence score of 24.60%. The vehicle detection accuracy of YOLO-V5s in
terms of mAP@50 of car detection is 88.10%, bus detection is 35.70%, truck detection is 77.40%, and
motorcycle detection is 99.50%. The overall mAP@50 is 75.20% for all vehicle detection classes. The
F1 score is 72%, with a confidence score of 24.60%. The vehicle detection accuracy of YOLO-V8s in
terms of mAP@50 of car detection is 89.40%, bus detection is 32.40%, truck detection is 72.90%, and
motorcycle detection is 99.50%. The overall mAP@50 is 73.50% for all vehicle detection classes. The
F1 score is 70%, with a confidence score of 12.30%. The vehicle detection accuracy of YOLO-V5m
in terms of mAP@50 of car detection is 89%, bus detection is 35.40%, truck detection is 69.80%, and
motorcycle detection is 99.50%. The overall mAP@50 is 73.40% for all vehicle detection classes. The
F1 score is 70% with a confidence score of 51.60%. The vehicle detection accuracy of YOLO-V8m in
terms of mAP@50 of car detection is 87.50%, bus detection is 34.20%, truck detection is 73.90%, and
motorcycle detection is 99.50%. The overall mAP@50 is 73.80% for all vehicle detection classes. The
F1 score is 70% with a confidence score of 18.50%. The vehicle detection accuracy of YOLO-V5l in
terms of mAP@50 of car detection is 91.80%, bus detection is 41.40%, truck detection is 76.40%, and
motorcycle detection is 99.50%. The overall mAP@50 is 77.30% for all vehicle detection classes. The
F1 score is 71%, with a confidence score of 51.90%. The vehicle detection accuracy of YOLO-V8l in
terms of mAP@50 of car detection is 89.20%, bus detection is 36.50%, truck detection is 65.10%, and
motorcycle detection is 99.50%. The overall mAP@50 is 72.60% for all vehicle detection classes. The
F1 score is 70%, with a confidence score of 14.90%. The vehicle detection results of YOLO-V5 and
YOLO-V8 on DAWN dataset are shown in Fig. 5.
Figure 5: (Continued)
922 CMC, 2024, vol.81, no.1
Figure 5: Vehicle detection results YOLO-V5 and YOLO-V8 on DAWN dataset
4.2 Performance Evaluation of Vehicle Detection Using YOLO-V5 and YOLO-V8 on the FD Dataset
The YOLO-V5 and YOLO-V8 (nano, small, medium, large) models are custom-trained on FD
dataset for four vehicle detection classes (car, bus, truck, and motorcycle) with the selected model and
training parameters, as highlighted in Table 1. The vehicle detection performance of YOLO-V5 and
YOLO-V8 on FD dataset is discussed in the following subsections. The vehicle detection accuracy of
YOLO-V5n in terms of mAP@50 of car detection is 62.90%, bus detection is 36.50%, truck detection
is 32.30%, and no instance of motorcycle present in the validation set leads to no detection results. The
overall mAP@50 is 43.90% for all vehicle detection classes. The F1 score is 38%, with a confidence
score of 14.70%. The vehicle detection accuracy of YOLO-V8n in terms of mAP@50 of car detection
is 68.70%, bus detection is 31.10%, truck detection is 24.90%, and no instance of motorcycle present in
CMC, 2024, vol.81, no.1 923
the validation set leads to no detection results. The overall mAP@50 is 41.60% for all vehicle detection
classes. The F1 score is 44%, with a confidence score of 26.60%. The vehicle detection accuracy of
YOLO-V5s in terms of mAP@50 of car detection is 71.60%, bus detection is 29.80%, truck detection
is 18.80%, and no instance of motorcycle present in the validation set leads to no detection results. The
overall mAP@50 is 40.10% for all vehicle detection classes. The F1 score is 38%, with a confidence
score of 13.40%. The vehicle detection accuracy of YOLO-V8s in terms of mAP@50 of car detection is
72.50 %, bus detection is 45.20%, truck detection is 25.10%, and no instance of motorcycle present in
the validation set leads to no detection results. The overall mAP@50 is 47.60% for all vehicle detection
classes. The F1 score is 44%, with a confidence score of 40.40%. The vehicle detection accuracy of
YOLO-V5m in terms of mAP@50 of car detection is 70.80%, bus detection is 39.90%, truck detection
is 38.20%, and no instance of a motorcycle in the validation set leads to no detection results. The overall
mAP@50 is 49.70% for all vehicle detection classes. The F1 score is 48%, with a confidence score of
42%. The vehicle detection accuracy of YOLO-V8m in terms of mAP@50 of car detection is 67.70%,
bus detection is 35.40%, truck detection is 25.70%, and no instance of a motorcycle in the validation
set leads to no detection results. The overall mAP@50 is 42.90% for all vehicle detection classes. The
F1 score is 48%, with a confidence score of 27.30%. The vehicle detection accuracy of YOLO-V5l in
terms of mAP@50 of car detection is 72.60%, bus detection is 30.90%, truck detection is 68.40%, and
no instance of a motorcycle in the validation set leads to no detection results. The overall mAP@50
is 57.30% for all vehicle detection classes. The F1 score is 53%, with a confidence score of 22.20%.
The vehicle detection accuracy of YOLO-V8l in terms of mAP@50 of car detection is 69.70%, bus
detection is 37.90%, truck detection is 26.90%, and no instance of a motorcycle in the validation set
leads to no detection results. The overall mAP@50 is 44.80% for all vehicle detection classes. The F1
score is 46%, with a confidence score of 57.10%. The vehicle detection results of all YOLO-V5 and
YOLO-V8 models on FD dataset are shown in Fig. 6.
Figure 6: (Continued)
924 CMC, 2024, vol.81, no.1
Figure 6: Vehicle detection results YOLO-V5 and YOLO-V8 on FD dataset
5Comparative Analysis and Discussion
Efficient and cost-effective Vehicle detection in foggy weather is extensively assessed on state-of-
the-art DAWN and FD datasets. The state-of-the-art YOLO-V5 and YOLO-V8 models with default
settings as well as proposed YOLO-V5s variants are custom trained and validated on cloud (Google
Colab) and edge (local) systems enabled with GPU resources. In cloud environment, the comparative
analysis of YOLO-V5 and YOLO-V8 models is summarized as follows. The YOLO-V5l outperformed
on DAWN dataset with precision score of 90.1%, recall score of 68.8%, mAP50 of 77.3%, mAP50-95
of 42.6%, and F1 score of 74% as compared to other YOLO-V5 and YOLO-V8 models. However,
YOLO-V5l underperform in FPS rate which is lower 30 FPS as compared to other YOLO-V5 and
YOLO-V8 models and only YOLO-V8n is dominated with 185 FPS. The YOLO-V5l outperformed
on FD dataset with recall score of 50%, mAP50 of 57.3%, mAP50-95 of 36.9%, and F1 score of
53% as compared to other YOLO-V5 and YOLO-V8 models. However, YOLO-V5l underperform
in FPS rate which is lower 17 FPS as compared to other YOLO-V5 and YOLO-V8 models and only
YOLO-V8n is dominated with 313 FPS. The results show that YOLO-V5 models are more efficient in
vehicle detection accuracy and YOLO-V8 models are more cost-effective in vehicle detection speed.
The experimental results of YOLO-V5 and YOLO-V8 vehicle detection performance on cloud (Google
Colab) for DAWN and FD datasets are tabulated in Tables 2 and 3, respectively.
CMC, 2024, vol.81, no.1 925
Table 2: Comparison of YOLO-V5 and YOLO-V8 on DAWN dataset
YOLO models P (%) R (%) mAP50
(%)
mAP50-95
(%)
F1 score
(%)
Pre-Proc
Time (ms)
Inference
time (ms)
NMS
time (ms)
FPS
rate
YOLO-V5n 84.70 64.80 72.60 37.70 71 0.3 6.1 10.7 59
YOLO-V5s 82.50 68.40 75.20 41.10 72 0.3 7.4 13.8 47
YOLO-V5m 81.20 66 73.40 41.90 70 0.3 14.5 11.7 38
YOLO-V5l 90.10 68.80 77.30 42.60 74 0.3 19.6 13.1 30
YOLO-V8n 67.90 57 60.20 35.70 62 0.3 2.5 2.6 185
YOLO-V8s 74.70 68.80 73.50 37.30 70 0.4 4.4 4.4 109
YOLO-V8m 73.20 68.50 73.80 40 70 0.2 10.5 3.2 72
YOLO-V8l 75.30 66.40 72.60 34.80 70 0.2 13.6 2.2 63
Table 3: Comparison of YOLO-V5 and YOLO-V8 on FD dataset
YOLO models P (%) R (%) mAP50
(%)
mAP50-95
(%)
F1 score
(%)
Pre-proc
time (ms)
Inference
time (ms)
NMS
time (ms)
FPS
rate
YOLO-V5n 32.10 47.20 43.90 20.30 38 0.3 6.4 31.1 26
YOLO-V5s 35 48.70 40.10 20.80 38 0.3 7.6 33.5 24
YOLO-V5m 58.10 41.40 49.70 27.50 48 0.3 14.2 30.4 22
YOLO-V5l 62.30 50 57.30 36.90 53 0.3 20.8 13.5 17
YOLO-V8n 49 37.20 41.60 21 44 0.2 2.1 0.9 313
YOLO-V8s 71.60 36.60 46.90 28.60 44 0.2 4.2 1.1 182
YOLO-V8m 67.70 39.90 42.90 27 48 0.2 8.9 1 99
YOLO-V8l 73.80 34.90 44.80 25 46 0.2 15 1.4 60
The cloud and edge (local) GPUs experimental results of YOLO-V5s and YOLO-V5s variants
on the DAWN dataset are presented in Table 4. On cloud GPU, state-of-the-art YOLO-V5s out-
performs and offer 82.5% precision, 75.2% of mAP50, 41.1% of mAP50-95, and 72% F1 score as
compared to proposed YOLO-V5s variants. The YOLO-V5s_CBAM offer higher recall score of
73.8% in comparison to all other models. However, proposed YOLO-V5s variants outperforms in pre-
processing, inference, and NMS time as well as offer higher FPS inference speed as compared to state-
of-the-art YOLO-V5s. On local GPU, proposed YOLO-V5s variants outperforms with competitive
results of precision, class level and overall mAP50 scores and underperforms in recall and F1
scores as compared to state-of-the-art YOLO-V5s. However, YOLO-V5s variants outperforms in pre-
processing, inference, and NMS time as well as offer higher FPS inference speed as compared to
state-of-the-art YOLO-V5s. The cloud and edge (local) GPUs experimental results of YOLO-V5s and
YOLO-V5s variants on the FD dataset are presented in Table 5. On cloud GPU, proposed YOLO-
V5s variants outperforms in precision and recall scores; buses, trucks, and overall mAP50 scores as
compared to the state-of-the-art YOLO-V5s. The YOLO-V5s in detecting cars; mAP50-95 and F1
scores as comparison to YOLO-V5s variants. However, proposed YOLO-V5s variants outperforms in
926 CMC, 2024, vol.81, no.1
pre-processing, inference, and NMS time as well as offer higher FPS inference speed as compared to
state-of-the-art YOLO-V5s.
Table 4: Vehicle detection results of YOLO-V5s and YOLO-V5s variants on DAWN dataset
YOLO Models P
(%)
R
(%)
Car
mAP
50 (%)
Bus
mAP
50 (%)
Motorcycle
mAP50
(%)
Truck
mAP50
(%)
Overall
mAP50
(%)
Overall
mAP50-
95 (%)
F1
score
(%)
Pre-proc
time
(ms)
Inference
time
(ms)
NMS
time
(ms)
Inference
speed
(FPS)
Cloud (Google Colab) NVIDIA Tesla T4 GPU–300 epochs
YOLO-V5s 82.5 68.4 88.1 35.7 99.5 77.4 75.2 41.1 72 0.3 7.4 13.8 47
YOLO-V5s_CBAM 47.7 73.8 86.7 25.1 99.5 73.5 71.2 35.8 61 0.3 8.0 12.9 47
YOLO-V5s_NAM 77.1 58.5 86.4 31.9 49.8 71.9 60 30.8 65 0.3 8.2 1.4 101
YOLO-V5s_SimAM 46.7 60.6 87.6 30.5 50.5 73.1 60.4 30 57 0.2 7.1 1.1 119
Edge (Local) NVIDIA GeForce GTX 1050 Ti GPU–300 epochs
YOLO-V5s 84 63.5 87.8 33 99.5 71.4 72.9 34.9 70 0.6 30.6 6.8 26
YOLO-V5s_CBAM 91.2 53.5 87.4 45.6 62.2 75.8 67.8 38.6 64 0.5 23.4 5.6 34
YOLO-V5s_NAM 73.7 56.8 87.3 36.2 75 76 68.6 34.2 59 0.6 22.5 6.1 34
YOLO-V5s_SimAM 74.3 57.3 87.8 46.4 75 75.4 71.1 36.8 59 0.4 23.1 3.9 37
Table 5: Vehicle detection results of YOLO-V5s and YOLO-V5s variants on FD dataset
YOLO models P
(%)
R
(%)
Car
mAP50
(%)
Bus
mAP50
(%)
Truck
mAP50
(%)
Overall
mAP50
(%)
Overall
mAP50-95
(%)
F1
score
(%)
Pre-proc
time
(ms)
Inference
time
(ms)
NMS
time
(ms)
Inference
speed
(FPS)
Cloud (Google Colab) NVIDIA Tesla T4 GPU–300 epochs
YOLO-V5s 35 48.7 71.6 29.8 18.8 40.1 20.8 38 0.3 7.6 33.5 24
YOLO-V5s_CBAM 30.5 63.2 70.8 37.9 14.3 41 20.1 40 0.3 7.2 36.9 23
YOLO-V5s_NAM 72.5 32.2 67.2 36.4 19.9 41.2 19.5 34 0.3 7.2 32.1 25
YOLO-V5s_SimAM 67.6 30.6 69.3 33 12.5 38.2 19 32 0.3 6.8 34.6 24
Edge (Local) NVIDIA GeForce GTX 1050 Ti GPU–300 epochs
YOLO-V5s 79.3 30.5 72.5 43.1 0 38.5 21.1 53 0.7 21.9 11.3 30
YOLO-V5s_CBAM 27.9 54.8 62.9 28.7 37.1 42.9 19.1 37 0.5 22.7 9.9 30
YOLO-V5s_NAM 30.1 53.4 72.1 30.3 34.6 45.7 21.2 38 0.7 21.9 10.5 30
YOLO-V5s_SimAM 63.2 35.9 70.1 31.5 15.2 38.9 19.9 36 0.7 22.1 10.1 30
On local GPU, proposed YOLO-V5s variants outperforms with competitive results of recall,
mAP50 and mAP50-95 scores as compared to state-of-the-art YOLO-V5s. The YOLO-V5s in detecting
cars and buses; and F1 scores as comparison to YOLO-V5s variants. However, YOLO-V5s variants
outperforms in pre-processing, inference, and NMS time as well as offer higher FPS inference speed
as compared to state-of-the-art YOLO-V5s. The cloud and edge (local) GPUs experimental results
of YOLO-V5l and YOLO-V5l_BiFPN on the DAWN dataset is presented in Table 6. On cloud
and local GPUs, YOLO-V5l mainly outperforms in precision score whereas, the YOLO-V5l_BiFPN
outperforms in recall, individual class level mAP50 scores, overall mAP50 score, mAP50-95 score
as well as F1 score. The YOLO-V5l_BiFPS is also outperforms in pre-processing, inference, and
NMS time as well as FPS inference speed as compared to YOLO-V5l. The cloud and edge (local)
GPUs experimental results of YOLO-V5l and YOLO-V5l_BiFPN on the FD dataset are presented
in Table 7. On cloud and local GPUs, YOLO-V5l outperforms in detecting all vehicle categories
CMC, 2024, vol.81, no.1 927
as compared to YOLO-V5l_BiFPN. YOLO-V5l_BiFPN outperforms in recall scores. The YOLO-
V5l_BiFPS outperforms in inference, and NMS time as well as FPS inference speed as compared to
YOLO-V5l. The comparison of YOLO-V5, YOLO-V5s variants, and YOLO-V8 with state-of-the-art
research on DAWN dataset is presented in Table 8.
Table 6: Vehicle detection results of YOLO-V5l and YOLO-V5l_BiFPN on DAWN dataset
YOLO models P
(%)
R
(%)
Car
mAP
50 (%)
Bus
mAP
50 (%)
Motorcycle
mAP50
(%)
Truck
mAP50
(%)
Overall
mAP50
(%)
Overall
mAP50-
95 (%)
F1
score
(%)
Pre-proc
time
(ms)
Inference
time
(ms)
NMS
time
(ms)
Inference
speed
(FPS)
Cloud (Google Colab) NVIDIA Tesla T4 GP–100 epochs
YOLO-V5l 91.7 68.3 90.3 38.6 99.5 78 76.6 41.6 74 0.3 16.8 16.6 30
YOLO-V5l_BiFPN 86.7 71.9 89.8 36.6 99.5 85.5 77.8 50.2 76 0.3 13.3 11.9 39
Edge (Local) NVIDIA GeForce GTX 1050 Ti GPU–100 epochs
YOLO-V5l 87.2 69.9 90.6 37.1 99.5 81.5 77.2 39.8 75 0.8 88.3 5.5 11
YOLO-V5l_BiFPN 81.4 74.1 90.6 51.1 99.5 78.7 80 42.8 76 0.5 87.4 4.2 11
Table 7: Vehicle detection results of YOLO-V5l and YOLO-V5l_BiFPN on FD dataset
YOLO models P (%) R (%) Car
mAP50
(%)
Bus
mAP50
(%)
Truck
mAP50
(%)
Overall
mAP50
(%)
Overall
mAP50-95
(%)
F1
score
(%)
Pre-proc
time
(ms)
Inference
time
(ms)
NMS
time
(ms)
Inference
speed
(FPS)
Cloud (Google Colab) NVIDIA Tesla T4 GPU–100 epochs
YOLO-V5l 41 57.8 77.2 57.1 38.4 57.5 33.6 50 0.3 21 38.2 17
YOLO-V5l_BiFPN 42.7 63 76.1 50.4 29.3 52 28.6 50 0.3 20.8 36 18
Edge (Local) NVIDIA GeForce GTX 1050 Ti GPU–100 epochs
YOLO-V5l 50.9 61 76.4 53.2 58.2 62.6 37 58 0.0 88.6 8.3 10
YOLO-V5l_BiFPN 43.2 68.9 74 52.8 37.4 54.7 29.3 53 1.0 70.9 7.3 13
Table 8: Comparison with State-of-the-art on DAWN dataset
Article/Reference Algorithm mAP50 (Car) mAP50 (Truck) mAP50
Our Experiments
YOLO-V5s 88.10% 77.40% 75.20%
YOLO-V5m 89% 69.80% 73.40%
YOLO-V5l 91.80% 76.40% 77.30%
YOLO-V8s 89.40% 72.90% 73.50%
YOLO-V8m 87.50% 73.90% 73.80%
YOLO-V8l 89.20% 65.10% 72.60%
[27]YOLO-V5 88.30% 75.60% —
YOLO-V5-TL 84.40% 75.60% —
[33] YOLO-V8 — — 60.40%
(Continued)
928 CMC, 2024, vol.81, no.1
Table 8 (continued)
Article/Reference Algorithm mAP50 (Car) mAP50 (Truck) mAP50
[50]
SSD +DA — — 25.50%
SSD-RRC — — 26.35%
Faster R-CNN — — 42.22%
GYOLOv3 — — 29.13%
YOLOv3 — — 44.11%
YOLOv8-CGW — — 72.98%
[51]R-CNN — — 27.20%
ResNet-101-FPN — — 28.95%
6Conclusion and Future Work
The challenge of vision-based vehicle detection in adverse weather conditions, particularly fog,
remains a critical concern for autonomous vehicles, collision avoidance systems, and IoT-enabled traf-
fic surveillance. This study evaluated the efficiency and cost-effectiveness of state-of-the-art YOLO-V5
and YOLO-V8 models, leveraging customized and self-annotated DAWN and FD datasets for vehicle
detection in foggy conditions. The introduction of YOLO-V5s variants—YOLO-V5s_CBAM, YOLO-
V5s_NAM, and YOLO-V5s_SimAM—along with the enhancement of YOLO-V5l using BiFPN,
demonstrated significant improvements in detection accuracy and speed, particularly in cloud-based
and edge environments. YOLO-V5l achieved superior performance in precision, recall, and mAP50
on the DAWN dataset, although it was outpaced by YOLO-V8 models in terms of FPS. This trade-off
between detection accuracy and processing speed highlights the need for careful selection of models
based on specific application requirements. Despite its strengths, YOLO-V5l’s lower FPS indicates a
need for further optimization, particularly in real-time applications. Future work will focus on refining
the backbone, neck, and head components of both YOLO-V5 and YOLO-V8 models. The goal is to
enhance vehicle detection accuracy while maintaining real-time processing capabilities. Additionally,
further research will explore more complex modifications to these models to achieve a better balance
between efficiency and cost-effectiveness, particularly for nano and small versions of the YOLO
models. This continued development will be crucial for advancing the practical deployment of vehicle
detection systems in challenging weather conditions.
Acknowledgement: This work was supported and funded by the Deanship of Scientific Research at
Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RG23129).
Funding Statement: This work was supported and funded by the Deanship of Scientific Research at
Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RG23129).
Author Contributions: The authors confirm contribution to the paper as follows: study concep-
tion and design: Naeem Raza, Mudassar Ahmad, Muhammad Ahsan Latif, Muhammad Asif
Habib, Qaisar Abbas; data collection, analysis and interpretation of results: Naeem Raza, Mudassar
Ahmad, Muhammad Asif Habib, Muhammad Ahsan Latif, Qaisar Abbas, Mutlaq B. Aldajani; draft
manuscript preparation: Naeem Raza, Qaisar Abbas, Muhammad Ahsan Latif, Mudassar Ahmad,
Muhammad Asif Habib. All authors reviewed the results and approved the final version of the
manuscript.
CMC, 2024, vol.81, no.1 929
Availability of Data and Materials: Data openly available in a public repository. The data that support
the findings of this study are openly available in DAWN at https://data.mendeley.com/datasets/766
ygrbt8y/3 and Foggy Driving at https://people.ee.ethz.ch/~csakarid/SFSU_synthetic/ (accessed on 03
January 2024). Furthermore, annotation files of DAWN and FD datasets are available with the
corresponding author and will be provided upon a reasonable request.
Ethics Approval: Not applicable.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
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