Conference PaperPDF Available

# A Survey on Performance Metrics for Object-Detection Algorithms

Authors:

## Abstract and Figures

This work explores and compares the plethora of metrics for the performance evaluation of object-detection algorithms. Average precision (AP), for instance, is a popular metric for evaluating the accuracy of object detectors by estimating the area under the curve (AUC) of the precision × recall relationship. Depending on the point interpolation used in the plot, two different AP variants can be defined and, therefore, different results are generated. AP has six additional variants increasing the possibilities of benchmarking. The lack of consensus in different works and AP implementations is a problem faced by the academic and scientific communities. Metric implementations written in different computational languages and platforms are usually distributed with corresponding datasets sharing a given bounding-box description. Such projects indeed help the community with evaluation tools, but demand extra work to be adapted for other datasets and bounding-box formats. This work reviews the most used metrics for object detection detaching their differences, applications, and main concepts. It also proposes a standard implementation that can be used as a benchmark among different datasets with minimum adaptation on the annotation files.
Content may be subject to copyright.
A Survey on Performance Metrics for
Object-Detection Algorithms
Rafael Padilla1, Sergio L. Netto2, Eduardo A. B. da Silva3
1,2,3PEE, COPPE, Federal University of Rio de Janeiro, P.O. Box 68504, RJ, 21945-970, Brazil
Abstract—This work explores and compares the plethora of
metrics for the performance evaluation of object-detection algo-
rithms. Average precision (AP), for instance, is a popular metric
for evaluating the accuracy of object detectors by estimating the
area under the curve (AUC) of the precision ×recall relationship.
Depending on the point interpolation used in the plot, two
different AP variants can be deﬁned and, therefore, different
results are generated. AP has six additional variants increasing the
possibilities of benchmarking. The lack of consensus in different
works and AP implementations is a problem faced by the academic
and scientiﬁc communities. Metric implementations written in
different computational languages and platforms are usually
distributed with corresponding datasets sharing a given bounding-
box description. Such projects indeed help the community with
evaluation tools, but demand extra work to be adapted for other
datasets and bounding-box formats. This work reviews the most
used metrics for object detection detaching their differences,
applications, and main concepts. It also proposes a standard
implementation that can be used as a benchmark among different
datasets with minimum adaptation on the annotation ﬁles.
Keywords—object-detection metrics, average precision, object-
detection challenges, bounding boxes.
I. INTRODUCTION
Object detection is an extensively studied topic in the ﬁeld
of computer vision. Different approaches have been employed
to solve the growing need for accurate object detection mod-
els [1]. The Viola-Jones framework [2], for instance, became
popular due to its successful application in the face-detection
problem [3], and was later applied to different subtasks such as
pedestrian [4] and car [5] detections. More recently, with the
popularization of the convolutional neural networks (CNN) [6]–
[9] and GPU-accelerated deep-learning frameworks, object-
detection algorithms started being developed from a new per-
spective [10], [11]. Works as Overfeat [12], R-CNN [13], Fast
R-CNN [14], Faster R-CNN [15], R-FCN [16], SSD [17] and
YOLO [18]–[20] highly increased the performance stantards
on the ﬁeld. World famous competitions such as VOC PAS-
CAL Challenge [21], COCO [22], ImageNet Object Detection
Challenge [23], and Google Open Images Challenge [24] have
as their top object-detection algorithms methods inspired on
the aforementioned works. Differently from algorithms such as
the Viola-Jones, CNN-based detectors are ﬂexible enough to be
trained with several (hundreds or even a few thousands) classes.
A detector outcome is commonly composed of a list of
bounding boxes, conﬁdence levels and classes, as seen in
Figure 1. However, the standard output-ﬁle format varies a
lot for different detection algorithms. Bounding-box detections
(a) (b)
(c)
Fig. 1: Examples of detections performed by YOLO [20] in
different datasets. (a) PASCAL VOC; (b) personal dataset; (c)
COCO. Besides the bounding box coordinates of a detected
object, the output also includes the conﬁdence level and its
class.
are mostly represented by their top-left and bottom-right co-
ordinates (xini, yini , xend, yend ), with a notable exception being
the YOLO [18]–[20] algorithm, that differs from the others by
outlining the bounding boxes by their center coordinates, width,
and height xcenter
image width ,ycenter
image height ,box width
image width ,box height
image height .
Different challenges, competitions, and hackathons [21],
[23]–[27] attempt to assess the performance of object de-
tections in speciﬁc scenarios by using real-world annotated
images [28]–[30]. In these events, participants are given a
testing nonannotated image set in which objects have to be
detected by their proposed works. Some competitions provide
their own (or 3rd-party) source code, allowing the participants
to evaluate their algorithms in an annotated validation image
set before submitting their testing-set detections. In the end,
each team sends a list of bounding-boxes coordinates with their
respective classes and (sometimes) their conﬁdence levels to be
evaluated.
In most competitions, the average precision (AP) and its
derivations are the metrics adopted to assess the detections
and thus rank the teams. The PASCAL VOC dataset [31] and
challenge [21] provide their own source code to measure the
AP and the mean AP (mAP) over all object classes. The City
Intelligence Hackathon [27] uses the source code distributed
in [32] to rank the participants also on AP and mAP. The Ima-
geNet Object Localization challenge [23] does not recommend
any code to compute their evaluation metric, but provides a
pseudo-code explaining it. The Open Images 2019 [24] and
Google AI Open Images [26] challenges use mAP, referencing
a tool to evaluate the results [33], [34]. The Lyft 3D Object
Detection for Autonomous Vehicles challenge [25] does not
reference any external tool, but uses the AP averaged over 10
different thresholds, the so-called AP@50:5:95 metric.
This work reviews the most popular metrics used to evalu-
ate object-detection algorithms, including their main concepts,
pointing out their differences, and establishing a comparison be-
tween different implementations. In order to introduce its main
contributions, this work is divided into the following topics:
Section II explains the main performance metrics employed
in the ﬁeld of object detection and how the AP metric can
produce ambiguous results; Section III describes some of the
most known object detection challenges and their employed
performance metrics, whereas Section IV presents a project
implementing the AP metric to be used with any annotation
format.
II. MAIN PERFORMANCE ME TR IC S
Among different annotated datasets used by object detection
challenges and the scientiﬁc community, the most common
metric used to measure the accuracy of the detections is the AP.
Before examining the variations of the AP, we should review
some concepts that are shared among them. The most basic are
the ones deﬁned below:
True positive (TP): A correct detection of a ground-truth
bounding box;
False positive (FP): An incorrect detection of a nonexistent
object or a misplaced detection of an existing object;
False negative (FN): An undetected ground-truth bounding
box;
It is important to note that, in the object detection context,
a true negative (TN) result does not apply, as there are inﬁnite
number of bounding boxes that should not be detected within
any given image.
The above deﬁnitions require the establishment of what a
“correct detection” and an “incorrect detection” are. A common
way to do so is using the intersection over union (IOU). It is
a measurement based on the Jaccard Index, a coefﬁcient of
similarity for two sets of data [35]. In the object detection
scope, the IOU measures the overlapping area between the
predicted bounding box Bpand the ground-truth bounding box
Bgt divided by the area of union between them, that is
J(Bp, Bgt) = IOU = area(BpBgt)
area(BpBgt),(1)
as illustrated in Figure 2.
Fig. 2: Intersection Over Union (IOU).
By comparing the IOU with a given threshold t, we can
classify a detection as being correct or incorrect. If IOU t
then the detection is considered as correct. If IOU < t the
detection is considered as incorrect.
Since, as stated above, the true negatives (TN) are not used in
object detection frameworks, one refrains to use any metric that
is based on the TN, such as the TPR, FPR and ROC curves [36].
Instead, the assessment of object detection methods is mostly
based on the precision Pand recall Rconcepts, respectively
deﬁned as
P=TP
TP +FP =TP
all detections ,(2)
R=TP
TP +FN =TP
all ground truths .(3)
Precision is the ability of a model to identify only relevant
objects. It is the percentage of correct positive predictions.
Recall is the ability of a model to ﬁnd all relevant cases (all
ground-truth bounding boxes). It is the percentage of correct
positive predictions among all given ground truths.
The precision ×recall curve can be seen as a trade-off
between precision and recall for different conﬁdence values
associated to the bounding boxes generated by a detector. If the
conﬁdence of a detector is such that its FP is low, the precision
will be high. However, in this case, many positives may be
missed, yielding a high FN, and thus a low recall. Conversely,
if one accepts more positives, the recall will increase, but the FP
may also increase, decreasing the precision. However, a good
object detector should ﬁnd all ground-truth objects (F N = 0
high recall) while identifying only relevant objects (F P = 0
high precision). Therefore, a particular object detector can
be considered good if its precision stays high as its recall
increases, which means that if the conﬁdence threshold varies,
the precision and recall will still be high. Hence, a high area
under the curve (AUC) tends to indicate both high precision
and high recall. Unfortunately, in practical cases, the precision
×recall plot is often a zigzag-like curve, posing challenges to
an accurate measurement of its AUC. This is circumvented by
processing the precision ×recall curve in order to remove the
zigzag behavior prior to AUC estimation. There are basically
two approaches to do so: the 11-point interpolation and all-
point interpolation.
In the 11-point interpolation, the shape of the precision
×recall curve is summarized by averaging the maximum
precision values at a set of 11 equally spaced recall levels [0,
0.1, 0.2, ... , 1], as given by
AP11 =1
11 X
R∈{0,0.1,...,0.9,1}
Pinterp(R),(4)
where
Pinterp(R) = max
˜
R:˜
RR
P(˜
R).(5)
In this deﬁnition of AP, instead of using the precision
P(R)observed at each recall level R, the AP is obtained
by considering the maximum precision Pinterp(R)whose recall
value is greater than R.
In the all-point interpolation, instead of interpolating only 11
equally spaced points, one may interpolate through all points
in such way that:
APall =X
n
(Rn+1 Rn)Pinterp(Rn+1 ),(6)
where
Pinterp(Rn+1 ) = max
˜
R:˜
RRn+1
P(˜
R).(7)
In this case, instead of using the precision observed at
only few points, the AP is now obtained by interpolating the
precision at each level, taking the maximum precision whose
recall value is greater or equal than Rn+1.
The mean AP (mAP) is a metric used to measure the
accuracy of object detectors over all classes in a speciﬁc
database. The mAP is simply the average AP over all classes
[15], [17], that is
mAP = 1
N
N
X
i=1
APi,(8)
with APibeing the AP in the ith class and Nis the total
number of classes being evaluated.
A. A Practical Example
As stated previously, the AP is calculated individually for
each class. In the example shown in Figure 3, the boxes
represent detections (red boxes identiﬁed by a letter - A,B,
..., Y) and the ground truth (green boxes) of a given class.
The percentage value drawn next to each red box represents
the detection conﬁdence for this object class. In order to
evaluate the precision and recall of the 24 detections among
the 15 ground-truth boxes distributed in seven images, an IOU
threshold tneeds to be established. In this example, let us
consider as a TP detection box one having IOU 30%. Note
that each value of IOU threshold provides a different AP metric,
and thus the threshold used must always be indicated.
Table I presents each detection ordered by their conﬁdence
level. For each detection, if its area overlaps 30% or more of
a ground truth (IOU 30%), the TP column is identiﬁed as
Fig. 3: Example of 24 detections (red boxes) performed by an
object detector aiming to detect 15 ground-truth objects (green
boxes) belonging to the same class.
1; otherwise it is set to 0 and it is considered as FP. Some
detectors can output multiple detections overlapping a single
ground truth (e.g. detections D and E in Image 2; G, H and
I in Image 3). For those cases the detection with the highest
conﬁdence is considered a TP and the others are considered
as FP, as applied by the PASCAL VOC 2012 challenge. The
columns Acc TP and Acc FP accumulate the total amount of
TP and FP along all the detections above the corresponding
conﬁdence level. Figure 4 depicts the calculated precision and
recall values for this case.
TABLE I: Computation of Precision and Recall Values for IOU
threshold = 30%
detection conﬁdence TP FP acc TP acc FP precision recall
R 95% 1 0 1 0 1 0.0666
Y 95% 0 1 1 1 0.5 0.0666
J 91% 1 0 2 1 0.6666 0.1333
A 88% 0 1 2 2 0.5 0.1333
U 84% 0 1 2 3 0.4 0.1333
C 80% 0 1 2 4 0.3333 0.1333
M 78% 0 1 2 5 0.2857 0.1333
F 74% 0 1 2 6 0.25 0.1333
D 71% 0 1 2 7 0.2222 0.1333
B 70% 1 0 3 7 0.3 0.2
H 67% 0 1 3 8 0.2727 0.2
P 62% 1 0 4 8 0.3333 0.2666
E 54% 1 0 5 8 0.3846 0.3333
X 48% 1 0 6 8 0.4285 0.4
N 45% 0 1 6 9 0.4 0.4
T 45% 0 1 6 10 0.375 0.4
K 44% 0 1 6 11 0.3529 0.4
Q 44% 0 1 6 12 0.3333 0.4
V 43% 0 1 6 13 0.3157 0.4
I 38% 0 1 6 14 0.3 0.4
L 35% 0 1 6 15 0.2857 0.4
S 23% 0 1 6 16 0.2727 0.4
G 18% 1 0 7 16 0.3043 0.4666
O 14% 0 1 7 17 0.2916 0.4666
As mentioned above, each interpolation method yields a
different AP result, as given by (Figure 5):
AP11 =1
11(1 + 0.6666 + 0.4285 + 0.4285 + 0.4285)
AP11 = 26.84%,
Fig. 4: Precision x Recall curve with values calculated for each
detection in Table I.
and (Figure 6):
APall = 1 (0.0666 0) + 0.6666 (0.1333 0.0666)
+ 0.4285 (0.40.1333) + 0.3043 (0.4666 0.4)
APall = 24.56%.
Fig. 5: Precision ×Recall curves of points from Table I using
the 11-point interpolation approach.
From what we have seen so far, benchmarks are not truly
comparable if the method used to calculate the AP is not
reported. Works found in the literature [1], [9], [12]–[20], [37]
usually neither mention the method used nor reference the
adopted tool to evaluate their results. This problem does not
occur much often in challenges, as it is a common practice
to have a reference software tool included in order for the
participants to evaluate their results. Also, it is not rare to
occur cases where a detector sets the same conﬁdence level
for different detections. Table I, for example, illustrates that
detections R and Y obtained the same conﬁdence level (95%).
Depending on the criterion used by a certain implementation,
one or other detection can be sorted as the ﬁrst detection in the
table, directly affecting the ﬁnal result of an object-detection
algorithm. Some implementations may consider the order that
Fig. 6: Precision ×Recall curves of points from Table I
applying interpolation with all points.
each detection was reported as the tiebreaker (usually one or
more evaluation ﬁles contain the detections to be evaluated),
but in general there is no common consensus by the evaluation
tools.
III. OBJECT-D ET EC TI ON CH AL LE NG ES A ND TH EI R AP
VARIANTS
Constantly, new techniques are being developed and new
different state-of-the-art object-detection algorithms are arising.
Comparing their results with different works is not an easy
task. Sometimes the applied metrics vary or the implementation
used by the different authors may not be the same, generating
dissimilar results. This section covers the main challenges and
their most popular AP variants found in the literature.
The PASCAL VOC [31] is an object-detection challenge
released in 2005. From 2005 to 2012, a new version of the
Pascal VOC was released with increased numbers of images
and classes, starting at four classes, reaching 20 classes in
its last update. The PASCAL VOC competition still accepts
submissions, revealing state-of-the-art algorithms for object
detections ever since. In this trail, the challenge applies the 11-
point interpolated precision (see Section II) and uses the mean
AP over all of its classes to rank the submission performances,
as implemented by the provided development kit.
The Open Images 2019 challenge [24] in its object-detection
track uses the Open Images Dataset [29] containing 12.2 M
annotated bounding boxes across 500 object categories on
1.7 M images. Due to its hierarchical annotations, the same
object can belong to a main class and multiple sub-classes
(e.g. ‘helmet’ and ‘football helmet’). Because of that, the users
should report the class and subclasses of a given detection. If
somehow only the main class is correctly reported for a detected
bounding box, the unreported subclasses affect negatively the
score, as it is counted as a false negative. The metric employed
by the aforementioned challenge is the mean AP over all classes
using the Tensorﬂow Object Detection API [33].
The COCO detection challenge (bounding box) [22] is a
competition which provides bounding-box coordinates of more
than 200,000 images comprising 80 object categories. The
submitted works are ranked according to metrics gathered into
four main groups.
AP: The AP is evaluated with different IOUs. It can be
calculated for 10 IOUs varying in a range of 50% to 95%
with steps of 5%, usually reported as AP@50:5:95. It also
can be evaluated with single values of IOU, where the
most common values are 50% and 75%, reported as AP50
and AP75 respectively;
AP Across Scales: The AP is determined for objects in
three different sizes: small (with area <322pixels),
medium (with 322<area <962pixels), and large (with
area >962pixels);
Average Recall (AR): The AR is estimated by the maxi-
mum recall values given a ﬁxed number of detections per
image (1, 10 or 100) averaged over IOUs and classes;
AR Across Scales: The AR is determined for objects in
the same three different sizes as in the AP Across Scales,
usually reported as AR-S, AR-M, and AR-L, respectively;
Tables II and III present results obtained by different object
detectors for the COCO and PASCAL VOC challenges, as given
in [20], [38]. Due to different bounding-box annotation formats,
researchers tend to report only the metrics supported by the
source code distributed with each dataset. Besides that, works
that use datasets with other annotation formats [39] are forced
to convert their annotations to PASCAL VOC’s and COCO’s
formats before using their evaluation codes.
TABLE II: Results using AP variants obtained by different
methods on COCO dataset [40].
methods AP@50:5:95 AP50 AP75 AP-S AP-M AP-L
Faster R-CNN with ResNet-101 [9], [15] 34.9 55.7 37.4 15.6 38.7 50.9
Faster R-CNN with FPN [15], [41] 36.2 59.1 39.0 18.2 39.0 48.2
Faster R-CNN by G-RMI [15], [42] 34.7 55.5 36.7 13.5 38.1 52.0
Faster R-CNN with TDM [15], [43] 36.8 57.7 39.2 16.2 39.8 52.1
YOLO v2 [19] 21.6 44.0 19.2 5.0 22.4 35.5
YOLO v3 [20] 33.0 57.9 34.4 18.3 35.4 41.9
SSD513 with ResNet-101 [9], [17] 31.2 50.4 33.3 10.2 34.5 49.8
DSSD513 with ResNet-101 [9], [44] 33.2 53.3 35.2 13.0 35.4 51.1
RetinaNet [40] 39.1 59.1 42.3 21.8 42.7 50.2
TABLE III: Results using AP variant (mAP) obtained by
different methods on PASCAL VOC 2012 dataset [38].
methods mAP
Faster R-CNN * [15] 70.4
YOLO v1 [18] 57.9
YOLO v2 ** [19] 78.2
SSD300 ** [17] 79.3
SSD512 ** [17] 82.2
(*) trained with PASCAL VOC dataset images only, while (**) trained with
COCO dataset images.
The metric AP50 in Table II is calculated in the same way as
the metric mAP in Table III, but as the methods were trained
and tested in different datasets, one obtains different results in
both evaluations. Due to the need of conversions between the
bounding-box annotations among different datasets, researchers
in general do not evaluate all methods with all possible metrics.
In practice, it would be more meaningful if methods trained and
tested with one dataset (PASCAL VOC, for instance) could also
be evaluated by the metrics employed in other datasets (COCO,
for instance).
IV. ANOPE N- SOURCE PERFORMANCE METRIC
REP OS ITORY
In order to help other researchers and the academic com-
munity to obtain trustworthy results that can be comparable re-
gardless the detector, the database, or the format of the ground-
truth annotations, a library was developed in Python with the
AP metric that can be extended to its variations. Easy-to-use
functions implement the same metrics used as benchmark by
the most popular competitions and object-detection researches.
The proposed implementation does not require modiﬁcations
of the detection model to match complicated input formats,
avoiding conversions to XML or JSON ﬁles. To assure the
accuracy of the results, the implementation followed to the
letter the deﬁnitions and our results were carefully compared
against the ofﬁcial implementations and the results are precisely
the same. The variations of the AP metric such as mAP, AP50,
AP75 and AP@50:5:95 using the 11-point or the all-point
interpolations can be obtained with the proposed library.
The input data (ground-truth bounding boxes and detected
bounding boxes) format was simpliﬁed requiring a single
format to compute all AP variation metrics. The format required
is straightforward and can support the most popular detectors.
For the ground-truth bounding boxes, a single text ﬁle for each
image should be created with each line in one of the following
formats:
<class> <left> <top> <right> <bottom>
<class> <left> <top> <width> <height>
For the detections, a text ﬁle for each image should include
a line for each bounding box in one of the following formats:
<class> <conﬁdence> <left> <top> <right> <bottom>
<class> <conﬁdence> <left> <top> <width> <height>
The second options support YOLO’s output bounding-box
formats. Besides specifying the input formats of the bounding
boxes, one can also set the IOU threshold used to consider
a TP (useful to calculate the metrics AP@50:5:95, AP50 and
AP75) and the interpolation method (11-point interpolation or
interpolation with all points). The tool will output the plots as
in Figures 5 and 6, the ﬁnal mAP and the AP for each class,
giving a better view of the results for each class. The tool
also provides an option to generate the output images with the
bounding boxes drawn on it as shown in Figure 1.
The project distributed with this paper can be accessed at:
far, our framework has helped researchers to obtain AP metrics
and its variations in a simple way, supporting the most popular
formats used by datasets, avoiding conversions to XML or
JSON ﬁles. The proposed tool has been used as the ofﬁcial
tool in the competition [27], adopted in 3rd-party libraries such
as [45] and used by many other works as in [46]–[48].
REFERENCES
[1] W. Hu, T. Tan, L. Wang, and S. Maybank, “A survey on visual surveil-
lance of object motion and behaviors,” IEEE Transactions on Systems,
Man, and Cybernetics, Part C: Applications and Reviews, vol. 34, no. 3,
pp. 334–352, Aug 2004.
[2] P. Viola and M. Jones, “Rapid object detection using a boosted cascade
of simple features,” in IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, vol. 1, Dec 2001, p. 511518.
[3] R. Padilla, C. Costa Filho, and M. Costa, “Evaluation of haar cascade
classiﬁers designed for face detection,” World Academy of Science,
Engineering and Technology, vol. 64, pp. 362–365, 2012.
[4] E. Ohn-Bar and M. M. Trivedi, “To boost or not to boost? on the limits
of boosted trees for object detection,” in IEEE International Conference
on Pattern Recognition, Dec 2016, pp. 3350–3355.
[5] Z. Sun, G. Bebis, and R. Miller, “On-road vehicle detection: A review,
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28,
no. 5, pp. 694–711, May 2006.
[6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classiﬁcation
with deep convolutional neural networks,” in International Conference on
Neural Information Processing Systems, 2012, pp. 1097–1105.
[7] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan,
V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,
in IEEE Conference on Computer Vision and Pattern Recognition, June
2015, pp. 1–9.
[8] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning
applied to document recognition,” in Proceedings of the IEEE, 1998, pp.
2278–2324.
[9] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for
image recognition,” in IEEE Conference on Computer Vision and Pattern
Recognition, Jun 2016, pp. 770–778.
[10] G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for
deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554,
2006.
[11] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of
data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507,
Jul. 2006.
[12] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Le-
Cun, “Overfeat: Integrated recognition, localization and detection using
convolutional networks,CoRR, 2013.
[13] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierar-
chies for accurate object detection and semantic segmentation,” in IEEE
Conference on Computer Vision and Pattern Recognition, Jun 2014.
[14] R. Girshick, “Fast r-cnn,” in IEEE International Conference on Computer
Vision, Dec 2015.
[15] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time
object detection with region proposal networks,” in Advances in Neural
Information Processing Systems 28, 2015, pp. 91–99.
[16] J. Dai, Y. Li, K. He, and J. Sun, “R-FCN: object detection via region-
based fully convolutional networks,CoRR, 2016.
[17] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C. Fu, and A. C.
Berg, “SSD: single shot multibox detector,CoRR, 2015.
[18] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once:
Uniﬁed, real-time object detection,” in IEEE Conference on Computer
Vision and Pattern Recognition, 2016, pp. 779–788.
[19] J. Redmon and A. Farhadi, “Yolo9000: Better, faster, stronger,” in IEEE
Conference on Computer Vision and Pattern Recognition, 2017, pp. 7263–
7271.
[20] ——, “Yolov3: An incremental improvement,Technical Report, 2018.
[21] M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams,
J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A
retrospective,International Journal of Computer Vision, vol. 111, no. 1,
pp. 98–136, Jan. 2015.
[22] Coco detection challenge (bounding box). [Online]. Available: https:
//competitions.codalab.org/competitions/20794
[23] ImageNet. Imagenet object localization challenge. [Online]. Available:
https://www.kaggle.com/c/imagenet-object-localization- challenge/
[24] G. Research. Open images 2019 - object detec-
tion challenge. [Online]. Available: https://www.kaggle.com/c/
open-images- 2019-object- detection/
[25] Lyft. Lyft 3d object detection for autonomous
vehicles. [Online]. Available: https://www.kaggle.com/c/
3d-object- detection-for-autonomous-vehicles/
[26] G. Research. Google ai open images - object de-
tection track. [Online]. Available: https://www.kaggle.com/c/
[27] City intelligence hackathon. [Online]. Available: https://belvisionhack.ru
[28] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang,
A. Karpathy, A. Khosla, M. Bernstein et al., “Imagenet large scale visual
recognition challenge,” International Journal of Computer Vision, vol.
115, no. 3, pp. 211–252, 2015.
[29] I. Krasin, T. Duerig, N. Alldrin, V. Ferrari, S. Abu-El-Haija,
A. Kuznetsova, H. Rom, J. Uijlings, S. Popov, S. Kamali, M. Malloci,
J. Pont-Tuset, A. Veit, S. Belongie, V. Gomes, A. Gupta, C. Sun,
G. Chechik, D. Cai, Z. Feng, D. Narayanan, and K. Murphy, “Openim-
ages: A public dataset for large-scale multi-label and multi-class image
classiﬁcation,” 2017.
[30] T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays,
P. Perona, D. Ramanan, P. Doll´
ar, and C. L. Zitnick, “Microsoft COCO:
common objects in context,” CoRR, 2014.
[31] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zis-
serman, “The pascal visual object classes (voc) challenge,” International
Journal of Computer Vision, vol. 88, no. 2, pp. 303–338, Jun. 2010.
[32] R. Padilla. Metrics for object detection. [Online]. Available: https:
[33] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S.
Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow,
A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur,
J. Levenberg, D. Man´
e, R. Monga, S. Moore, D. Murray, C. Olah,
M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker,
V. Vanhoucke, V. Vasudevan, F. Vi´
egas, O. Vinyals, P. Warden, M. Watten-
berg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine
learning on heterogeneous systems,” 2015.
[34] TensorFlow. Detection evaluation protocols. [Online]. Available: https:
//github.com/tensorﬂow
[35] P. Jaccard, ´
Etude comparative de la distribution ﬂorale dans une portion
des alpes et des jura,” Bulletin de la Societe Vaudoise des Sciences
Naturelles, vol. 37, pp. 547–579, 1901.
[36] J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a
pp. 29–36, 1982.
[37] D. Yoo, S. Park, J.-Y. Lee, A. S. Paek, and I. So Kweon, “Attentionnet:
Aggregating weak directions for accurate object detection,” in IEEE
International Conference on Computer Vision, 2015, pp. 2659–2667.
[38] Z.-Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu, “Object detection with deep
learning: A review,” IEEE Transactions on Neural Networks and Learning
Systems, vol. 30, no. 11, pp. 3212–3232, 2019.
[39] “An annotated video database for abandoned-object detection in a clut-
tered environment,” in International Telecommunications Symposium,
2014, pp. 1–5.
[40] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Doll´
ar, “Focal loss for
dense object detection,” in IEEE International Conference on Computer
Vision, 2017, pp. 2980–2988.
[41] T.-Y. Lin, P. Doll´
ar, R. Girshick, K. He, B. Hariharan, and S. Belongie,
“Feature pyramid networks for object detection,” in IEEE Conference on
Computer Vision and Pattern Recognition, 2017, pp. 2117–2125.
[42] J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer,
modern convolutional object detectors,” in IEEE Conference on Computer
Vision and Pattern Recognition, 2017, pp. 7310–7311.
[43] A. Shrivastava, R. Sukthankar, J. Malik, and A. Gupta, “Beyond skip
connections: Top-down modulation for object detection,” arXiv, 2016.
[44] C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg, “Dssd: Deconvo-
lutional single shot detector,arXiv, 2017.
[45] C. R. I. of Montreal (CRIM). thelper package. [Online]. Available:
[46] C. Adleson and D. C. Conner, “Comparison of classical and cnn-based
detection techniques for state estimation in 2d,” Journal of Computing
Sciences in Colleges, vol. 35, no. 3, pp. 122–133, 2019.
[47] A. Borji and S. M. Iranmanesh, “Empirical upper-bound in object
detection and more,” arXiv, 2019.
[48] D. Caschili, M. Poncino, and T. Italia, “Optimization of cnn-based
object detection algorithms for embedded systems,” Masters dissertation,
Politecnico di Torino, 2019.
... Mean average precision (mAP) is a metric used to measure object detection accuracy and is the mean of the average precision (AP) of all classes in the database [53]. To obtain the AP, we must first understand the relationship between precision and recall, which can ...
... Mean average precision (mAP) is a metric used to measure object detection accuracy and is the mean of the average precision (AP) of all classes in the database [53]. To obtain the AP, we must first understand the relationship between precision and recall, which can be defined as shown in Figure 4. ...
... Mean average precision (mAP) is a metric used to measure object detection accuracy and is the mean of the average precision (AP) of all classes in the database [53]. To obtain the AP, we must first understand the relationship between precision and recall, which can be defined as shown in Figure 4. True positive is defined as a correct detection by predicting actual targets. ...
Article
Full-text available
Rivers are generally classified as either national or local rivers. Large-scale national rivers are maintained through systematic maintenance and management, whereas many difficulties can be encountered in the management of small-scale local rivers. Damage to embankments due to illegal farming along rivers has resulted in collapses during torrential rainfall. Various fertilizers and pesticides are applied along embankments, resulting in pollution of water and ecological spaces. Controlling such activities along riversides is challenging given the inconvenience of checking sites individually, the difficulty in checking the ease of site access, and the need to check a wide area. Furthermore, considerable time and effort is required for site investigation. Addressing such problems would require rapidly obtaining precise land data to understand the field status. This study aimed to monitor time series data by applying artificial intelligence technology that can read the cultivation status using drone-based images. With these images, the cultivated area along the river was annotated, and data were trained using the YOLOv5 and DeepLabv3+ algorithms. The performance index mAP@0.5 was used, targeting >85%. Both algorithms satisfied the target, confirming that the status of cultivated land along a river can be read using drone-based time series images.
... Deep Learning (DL) find diverse and extensive applications from intelligence analysis to IoT, Computer vision and cybersecurity and autonomous driving, UAVs [1]. The next-generation Intelligent systems rely on Artificial Intelligence (AI) to effectively perform various tasks like object detection, object classification, tracking of objects, and prediction [2]. DL provides computational resources to learn without explicitly programming to support intelligent systems. ...
... . Intersection over Union (IoU): recognized as Jaccard index, this measure evaluates the intersection between two bounding boxes, a ground truth bounding box, and the predicted bounding box. With this definition, a prediction can be classified into valid (TP) or invalid (FP) (Equation 2.1)[53]. True Positive (TP): is a correct detection. ...
Thesis
Full-text available
Thesis about Traffic Signs detection based on deep learning and implementation it on Raspberry Pi 4
... and evaluate the ability of the detector to find all relevant objects and to correctly detect objects (Padilla et al., 2020). Precision is the ability of the detector to handle only the objects expressed by the detector. ...
Article
Full-text available
D.W. Lee, S.P. Choi, J.H. Lee and W.K. Chung. Efficient seismic numerical modeling technique using the YOLOv2-based expanding domain method. Journal of Seismic Exploration, 31: 425-449. Wave equation-based seismic modeling has the advantage of simulating the exact full-wave propagation. However, it requires a great amount of computational resources, which becomes prohibitive when both the modeling domain size and the number of the time samples increase. Therefore, much research has been performed to enhance the computational efficiency of seismic numerical modeling. The expanding domain method is such one technique that improves the computational efficiency by identifying the domain extent where the wave propagation has not reached and excluding these domains from the calculation. In this work, we propose a new deep-learning based method that guide the determination of the computational domain. In order to establish the computational domain where the wave propagates from the snapshots, the deep learning-based object detection was employed. The deep learning object detector used has two main components. The first one is a structure for the feature extraction layers based on ResNet-50. The second one is a structure for the detection of the wave propagation domain based on the You Only Look Once method, version 2 (YOLOv2). After the training, validation and test for the YOLOv2 object detector, the computational efficiency of our proposed method was compared with that of the widely used amplitude comparison-based expanding domain method. It was demonstrated that the computational efficiency of the YOLOv2 method was better when the number of modeling grids was large, and the efficiency in the largest number of grids was about 25.1 %. KEY WORDS: seismic numerical modeling, computational efficiency, expanding domain method, deep learning object detection, YOLOv2.
... As followed in literature [7], we used accuracy (A), precision (P), recall (R), f1-score (f1) and mean average precision (mAP@k) for the tag detection task at different intersection over union (IoU) thresholds (represented by k). We treat digit recognition as a classification task, therefore we used macro and weighted average accuracy (A), precision (P), recall (R) and f1-score (f1) over all the classes as evaluation metrics [3]. ...
Preprint
A virtual or digital tour is a form of virtual reality technology which allows a user to experience a specific location remotely. Currently, these virtual tours are created by following a 2-step strategy. First, a photographer clicks a 360 degree equirectangular image; then, a team of annotators manually links these images for the "walkthrough" user experience. The major challenge in the mass adoption of virtual tours is the time and cost involved in manual annotation/linking of images. Therefore, this paper presents an end-to-end pipeline to automate the generation of 3D virtual tours using equirectangular images for real-estate properties. We propose a novel HSV-based coloring scheme for paper tags that need to be placed at different locations before clicking the equirectangular images using 360 degree cameras. These tags have two characteristics: i) they are numbered to help the photographer for placement of tags in sequence and; ii) bi-colored, which allows better learning of tag detection (using YOLOv5 architecture) in an image and digit recognition (using custom MobileNet architecture) tasks. Finally, we link/connect all the equirectangular images based on detected tags. We show the efficiency of the proposed pipeline on a real-world equirectangular image dataset collected from the Housing.com database.
... The "Metric" column of Table 1 reports the performance metrics used by the authors when these reflect common options used for CV (Padilla et al., 2020;Wambugu et al., 2021) and are unambiguous. ...
Article
Full-text available
Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies. However, a systematic review to describe the state-of-the-art of the field is lacking. Here we provide such a review, and we highlight current knowledge gaps and suggest promising future research directions. The review compares 34 papers with respect to their application and modeling related criteria. The results show that the researchers have employed a variety of DL architectures implementing different CV techniques to detect macroplastic litter in various aquatic environments. However, key knowledge gaps must be addressed to overcome the lack of: (i) DL-based macroplastic litter detection models with sufficient generalization capability, (ii) DL-based quantification of macroplastic (mass) fluxes and hotspots and (iii) scalable macroplastic litter monitoring strategies based on robust DL-based quantification. We advocate for the exploration of data-centric artificial intelligence approaches and semi-supervised learning to develop models with improved generalization capabilities. These models can boost the development of new methods for the quantification of macroplastic (mass) fluxes and hotspots, and allow for structural monitoring strategies that leverage robust DL-based quantification. While the identified gaps concern all bodies of water, we recommend increased efforts with respect to riverine ecosystems, considering their major role in transport and storage of litter.
Article
Full-text available
The main contribution of this paper is an approach for introducing additional context into state-of-the-art general object detection. To achieve this we first combine a state-of-the-art classifier (Residual-101[14]) with a fast detection framework (SSD[18]). We then augment SSD+Residual-101 with deconvolution layers to introduce additional large-scale context in object detection and improve accuracy, especially for small objects, calling our resulting system DSSD for deconvolutional single shot detector. While these two contributions are easily described at a high-level, a naive implementation does not succeed. Instead we show that carefully adding additional stages of learned transformations, specifically a module for feed-forward connections in deconvolution and a new output module, enables this new approach and forms a potential way forward for further detection research. Results are shown on both PASCAL VOC and COCO detection. Our DSSD with $513 \times 513$ input achieves 81.5% mAP on VOC2007 test, 80.0% mAP on VOC2012 test, and 33.2% mAP on COCO, outperforming a state-of-the-art method R-FCN[3] on each dataset.
Article
Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.
Conference Paper
Can a large convolutional neural network trained for whole-image classification on ImageNet be coaxed into detecting objects in PASCAL? We show that the answer is yes, and that the resulting system is simple, scalable, and boosts mean average precision, relative to the venerable deformable part model, by more than 40% (achieving a final mAP of 48% on VOC 2007). Our framework combines powerful computer vision techniques for generating bottom-up region proposals with recent advances in learning high-capacity convolutional neural networks. We call the resulting system R-CNN: Regions with CNN features. The same framework is also competitive with state-of-the-art semantic segmentation methods, demonstrating its flexibility. Beyond these results, we execute a battery of experiments that provide insight into what the network learns to represent, revealing a rich hierarchy of discriminative and often semantically meaningful features.
Conference Paper
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry
Conference Paper
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region pro-posal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully-convolutional network that simultaneously predicts object bounds and objectness scores at each position. RPNs are trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. With a simple alternating optimization, RPN and Fast R-CNN can be trained to share convolu-tional features. For the very deep VGG-16 model [18], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007 (73.2% mAP) and 2012 (70.4% mAP) using 300 proposals per image. The code will be released.