SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 4(2); September (2023), pp. 80–88
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

Optimizing Traffic Sign Detection System Using Deep Residual
Neural Networks Combined with Analytic Hierarchy Process
Model
Hanlin Cai1, Zheng Li1, Jiaqi Hu1, Wei Hong Lim2, Sew Sun Tiang2, Mastaneh Mokayef2, Chin Hong Wong1
1

Maynooth International Engineering College, Fuzhou University, Fujian, China
Faculty of Engineering, Technology and Built Environment, UCSI University, 1, Jalan Puncak Menara Gading, UCSI Heights, 56000 Cheras, Kuala Lumpur,
Malaysia.
2

ARTICLE INFO
Article History
Received 05 December 2022
Accepted 07 October 2023
Keywords
Traffic sign detection system
Residual neural network (ResNet)
Analytic hierarchy process (AHP)

ABSTRACTThis paper utilises image pre-processing techniques and deep residual neural networks to enhance
the traffic sign detection system. A novel Analytic Hierarchy Process (AHP) model for
performance evaluation has been proposed and utilised to determine the optimal parameter
configuration of the learning models. Four evaluation metrics, namely accuracy, stability,
response time, and system capability, have been defined for AHP measurements. The experiments
were conducted using a comprehensive dataset, with VGG-16 and Google Net implemented forcomparisons. Finally, the combination of ResNet-50 and the AHP model yielded the best results,
achieving a 98.01% accuracy rate, 0.09% false alarm rate, and 1.28% undetection rate.
© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

Network (CNN) model that possessed a very highdetection accuracy of nearly 98.8%. Changzhen, et al. [5]implemented an advanced detection method based on a
deep CNN model which achieved a result of over 99.0%
recognition precision.
Despite the impressive performance of deep learningmodels, the effectiveness of the CNN model will
decrease when confronted with trickier recognition
challenges which require deeper layers and morecomputing resources [3]. The Residual Neural Network(ResNet) was proposed by He, et al [6] to resolve theproblem that the performance decreases with the
deepening of network training. This model employs aresidual learning approach that substantially mitigates thethe challenges of training deep networks. Moreover,
ResNet models have been widely applied in various
research works. Zakaria, et al. [7] utilized ResNets in the
medical field to recognize and classify medical images.
Li and Rai [8] optimized and enhanced the model

1. Introduction
Deep Learning plays a pivotal role in current frontierscience and finds widespread applications in sectors suchas manufacturing, agriculture and transportation. In thecontent of urban transportation, it is of great significanceto leverage deep learning approaches for the Traffic
Signs Detection System (TSDS), which normallycomprises two related domains: Traffic Sign Detection(TSD) and Traffic Sign Recognition (TSR). However,
TSDS application demand high accuracy and precision
while minimising detection and recognition time. As analternative to conventional detection methods, deeplearning-based approaches appear to be a promising
option for efficient traffic sign detection [1], [2], [3].
According to existing research, to address the challengeof traffic sign detection and recognition, Suriya Prakash,
et al. [4] proposed a LeNet-5 Convolutional Neural

Corresponding author’s E-mail: hanlin.cai@ieee.org, zheng.li.2021@mumail.ie, jiaqi.hu.2021@mumail.ie, limwh@ucsiuniversity.edu.my,tiangss@ucsiuniversity.edu.my, mastaneh@ucsiuniversity.edu.my, chinhong.wong@mu.ie

80

structures in the study of fruit leaf disease recognitionand classification, yielding an impressive accuracy rate
exceeding 98.2%.
This paper leverages the deep residual neuralnetworks to explore state-of-the-art solutions for traffic
sign detection. In this work, image pre-processing
techniques and ResNet models were implemented to
conduct detection experiments. In addition, a novel
Analytic Hierarchy Process (AHP) model was introduced
for performance assessment and comparison. Extensive
experiments based on real-world datasets were
performed to validate the effectiveness of proposed
method.
The rest of this paper is organized as follows. Secton
2 provides a brief introduction to related research.
Section 3 outlines the workflow and structure of the
proposed detection system. Section 4 presents theperformance evaluation method based on the AHP model.
Section 5 is dedicated to the discussion of experimental
results and finally, Section 6 summarises the conclusionsof this paper.

an exhaustive overview of the traditional methods andpointed to many problems regarding traditional image
detection and recognition methods. Given the limitationsof conventional methods, most have gradually been
replaced by new learning-based models, which optimise
performance and effectiveness by using existing datasets
and prior experience.
Over the past two decades, numerous learning-based
approaches have been introduced to tackle intricate
TSDS challanges. Support Vector Machine (SVM)
models have been utilized in Spanish traffic signdetection to issue alerts to drivers [18]. Neural networkmodels also gained extensive attention in this domain,often combined with techniques such as Houghtransformation, corner detection and projection methods.
Models proposed by Kuo and Lin [19] achieved good
accuracy of nearly 95.5% based on the traffic sign
datasets in Taiwan, China. However, as traffic senariors
become more complex and the number of signal
categories increases, conventional machine learningmodels struggle to handle more intricate challenges, likecontaminated, multi-object, and large-scale signdetection and recognition [9].

2. Related Works
2.1. Traffic Sign Detection System (TSDS)

2.2. Deep Learning TechniqueTraffic signs play a crucial role in providing essential
information for safe driving in real-world scenarios.
Variousmethods and algorithms have been developed todetect and recognize different traffic signs in many
countries and regions. In general, the TSDS concern two
related aspects: Traffic Sign Detection (TSD), whichaims to accurately locate traffic signs in the physicalenvironment, and Traffic Sign Recognition (TSR), which
focuses on identifying the meaning of specific traffic
signs such as speed limits, stop signs, and directionalsigns. As for existing works, Lu, et al. [9], Wali, et al.
[10] and Arcos-García, et al. [11] have conductedcomprehensive surveys of state-of-the-art techniques in
the field of TSDS.
As illustrated in Table 1, this paper provides anextensive review of relevant literature. In the realm of
conventional TSDS methods, the majority of research
efforts concentrate on color segmentation, image shape,
and texture features [12], [13], [14], [15], [16], [17].
Nevertheless, these conventional approaches heavily rely
on image quality, rendering them vulnerable to variations
caused by factors such as daylight conditions and theimpact of environmental pollutants on sign paint.
Recognizing these limitations, Fleyeh, et al [1] conducted

Deep learning (DL) techniques have been at theforefront of computer vision, finding extensive
applications in image detection and classification [2].
Among the various DL approaches, Convolutional
Neural Networks (CNN) are one of the most prominentmodels in the field of TSDS.
Suriya Prakash, et al. [4] extended and enhanced theclassical LeNet-5 CNN model by incorporating a Gaborbased kernel followed by a standard convolutional kernel
after the pooling layer. Their proposed CNN model was
evaluated using the German Traffic Sign Benchmark,achieving an accuracy of approximately 98.9%.
Furthermore, Changzhen, et al. [5] introduced anovel algorithm based on deep CNN with a Region
Proposal Network (RPN) for the detection of all Chinese
traffic signs. The experiments demonstrated that theirmodel achieved near real-time detection speed andprecision exceeding 99.0%.
To achieve improved detection response times, K R,
et al. [20] introduced a combined approach that utilises
both Faster Region-based Convolution Neural Network(R-CNN) and RPN network. Additionally, the Random
Forest algorithm is employed for classification and81regression within the provided dataset. These composite
methods substantially decreased the resource demands
for training deep learning models while increasing
accuracy to up to 99.9%.
Many existing methods typically concentrate on a
restricted set of traffic sign classes, covering around 50
out of several hundred categories found in various
regions. To improve this limitation, Tabernik and Skocaj[21] proposed an enhanced mask R-CNN model toresolve the challenge of detecting a large-scale range oftraffic sign categories. Their experiments were conducted
on a large-scale sign dataset, and the results demonstrated
an average error rate below 3% in real-world detections.
However, it is noted that a systematic performance
evaluation method is still lacking in most of the research[22].

evaluated using a novel AHP method. The TsinghuaTencent 100K dataset [23], comprising approximately
100k images with about 30k traffic signs, was employed
for training and testing. The workflow of poposed traffic
sign detection system is shown in Fig. 1, with 80% of thedataset used for model training and the remaining 20%
for performance evaluation.
ResNet-50
80%

Training
DatasetTraffic
Sign

Image
Preprocess

Training

ResNet-101

RNN
Model

ResNet-152

Dataset20%

AHP Method

Testing
DatasetTraffic
Sign

Image
Preprocess

Comparewith model

Most Probable
Class

Fig. 1. Workflow of model training and testing process

3.1. Image Pre-processing
Table 1 A summary of related research works
Techniques
Colour Segmentation [12], [13]Descriptions
Easily affected by daylight conditions

Texture Features [16], [17]Highly reliant on the quality of the images

SVM Classifier [18]Good classification accuracy but low speed

NN Models [19]High accuracy, but large resources are required

LeNet-5 CNN [4]Using Gabor Based Kernel. High accuracy

CNN+RPN [5]Fast real-time sign detection speed

R-CNN+RF [20]High accuracy, recall and F1-score

Mask R-CNN [21]Implemented based on a large-scale dataset

ResNet+AHP [22]Proposed a systematic evaluation model

The first stage of the TSDS workflow is the image
pre-processing. Given that pixels with sudden changes inbrightness in an image usually represent the contours ofan object, precise localization of these pixels allows for
accurate recognition and prediction of actual signs [21].
To extract image features, this paper primarily employededge detection and corrosion expansion. The flowchart ofthe image pre-processing techniques is illustrated in thefollowing Fig. 2.
Image Preprocess

2.3. Research Gap and Further WorkDS
DatasetTo date, there has been limited research dedicated tothe training and testing ResNet models using
comprehensive traffic sign datasets that encompass awide range of categories. This paper introduces deep
ResNets to address the challenges of large-scale signdetection, and experiments are conducted to validate its
effectiveness in enhancing the TSDS. Furthermore,
despite the favorable detection results obtained inprevious research, the majority of evaluations haveconcentrated exclusively on essential metrics such asdetection accuracy, precision, and response time. This
paper presents a novel AHP approach to evaluate thepractical performance of deep learning models acrossdifferent parameters and optimizers.

Noise
Processing

Gradient
Computation

Non-maximum Double Threshold Corrosion
Suppression
Detection
Expansion

Sign

Fig. 2. Flowchart of the image pre-processing

3.1.1. Edge Detection(i) Noise processing & Gradient computation
In images, pixels exhibiting pronounced variations ingrayscale values may manifest as noise, which canappear randomly. Such noise can be generated fromvarious sources, including image acquisition,transmission, and quantization. To mitigate the impact ofnoise, this paper employs a Gaussian filter for noise
reduction. It is important to note that while the Gaussian
filter effectively reduces noise, it can have an adverse
effect on image definition and sharpness. To address this,gradient computation is subsequently applied to extract
object outlines within the image. These two sequential

3. Proposed Methodology
In this paper, the ResNet models with 50, 101, and 152
layers of CNN-based architectures were established and82

3.2. Residual Neural Network (ResNet)

processes enhance the saliency of image features,
rendering them more suitable for subsequent processing
procedures.

Following image pre-propossing, the next stage is toclassify the traffic sign into the most probable category.
In this work, the sign recognition is conducted using
ResNet model, which consists of two different layers,
known as the conv block and identity block. These blocks
serve as shortcuts in residual blocks and are arranged in
the order shown in Fig.3.

(ii) Non-maximum suppression
After gradient computation, the features of the main
object are enhanced, and the non-maximal value
suppression is used to eliminate the influence of other
non-target objects. This technique retains only local
maximum gradient values, indicating the positions of thestrongest image features. In areas with multiple traffic
signs, over-lapping detections may occur. Non-maximal
values help identify the optimal target boundary frame
and remove redundant boundary boxes.

Fig. 4 illustrates the structures of the identity andconv blocks. Each residual block consists of a stack of
three layers: 1 × 1, 3 × 3, and 1 × 1 convolution layers.
The 1 × 1 layers are responsible for dimension reduction
and subsequent dimension expansion, while the 3 × 3
layer operates with smaller input and output dimensions.

(iii) Double threshold detection
The final step in edge detection involves
differentiating objects from the background. It utilizes
the grayscale contrast between the target and its
background in the image to extract the desired traffic
signs. This is achieved by classifying pixel values into
multiple categories through thresholding, thereby
effectively separating objects from the background.
3.1.2. Corrosion expansion
After edge detection, the next step involves corrosion
expansion, which entails selecting the maximum value
within the neighborhood of each pixel to determine theoutput grayscale value. This expansion process enhances
the overall brightness of the image, making brighter
objects appear larger, while darker objects may be
reduced or even eliminated. By merging the outcomes ofimage processing with the location identified throughcolor thresholding, a sign devoid of background noise is
obtained.
Conv1
Conv Batch ReLu Max
Poll
Norm
Sign

Conv4

Most
Probable
Class

Conv
Block

Fig. 4. Structures of the identity and conv blocks

4. Analytic Hierarchy Process (AHP)
To effectively access the maturity level of different
process component and parameter configuration, thispaper used the AHP to develop a maturity evaluation
model. AHP leverages straightforward mathematical
tools in conjunction with operational concepts to break
down intricate problems into individual elements andconstruct hierarchies based on their interdependencies.
The key mathematical notations utlized in this paper areprovided in the following Table 2.

Conv2
Identity
Block

Conv
Block

Table 2. Notations of the AHP method
SymbolDescription
Unit
Score
Total score
1
RT
Response time score
1
SB
Stability score
1
AC
Accuracy score
1
System capability
SC
1
score
Piture/ms
PS
Process Speed
Computing
FLOPS
CPR
Requirement

Conv3
Identity
Block

Identity
Block

Conv
Block

Fig. 3. Architecture of the applied ResNet models

834.1. Maturity level evaluation model

Process Speed (PS) & Response Time (RT)This paper defines four evaluation indicators to
calculate the maturity level score of different models.
These four sub-indicators are given as follows: Accuracy(AC), Stability (SB), Response Time (RT) and System
Capability (SC).

To ensure the efficiency of the process parts, theresponse time is a critical factor. The training speed isdetermined by the time of processing 1000 images basedon Equation (3). A shorter training time results in ahigher score, as indicated in Table 5.

Accuracy (AC)𝑃𝑆 =The most crucial criterion for evaluating the model isthe accuracy. Therefore, the ratio between the number ofcorrect detection and the total dataset is used to normalize
the performances of images in different scales. The
corresponding score in Table 3 is calculated as follows:

𝐴𝐶 =𝐶𝑜𝑟𝑟𝑒𝑐𝑡 𝑛𝑢𝑚𝑏𝑒𝑟
× 100%
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟(1)

System capability (SC)
Hardware requirements are essential factors that limit
the performance of models. These requirements are
determined by the computing power requirement (CPR).
Floating-point operations per second (FLOPS) are often
used to estimate the performance of computer, especiallyin scientific computing scenarios, where many floatingpoint arithmetic operations are performed. Therefore, thispaper also employs FLOPS to evaluate the level of CPR,
as illustrated in Table 6.

Stability (SB)The stability of detection model is crucial in various
working conditions. However, unexpected factors candisrupt the detection process in realistic scenarios, suchas low-light environments, incomplete images, anddamaged traffic signs. To assess stability, this paper
calculates the ratio between accuracy under realistic
conditions and accuracy under ideal conditions. The SBscore in Table 4 can be calculated by:𝑃𝑟𝑎𝑐𝑡𝑖𝑐𝑎𝑙 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦
× 100%
𝑇ℎ𝑒𝑎𝑡𝑟𝑖𝑐𝑎𝑙 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦(3)

Table 5 Response time score table
Process Speed
RT (Score)More than 3.00s
02.40 ~ 3.00s
11.80 ~ 2.40s
21.20 ~ 1.80s
30.60 ~ 1.20s
40.00 ~ 0.60s
5Table 3 Accuracy score table
Accuracy
AC (Score)
Less than 75%
075% ~ 80%1
80% ~ 85%2
85% ~ 90%3
90% ~ 95%
4More than 95%
5

𝑆𝐵 =𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑡𝑖𝑚𝑒
1000

Table 6 System capability score table
CPR
SC (Score)More than 3.50G
02.00 ~ 3.50G
1
1.50 ~ 2.00G
21.00 ~ 1.50G
30.50 ~ 1.00G
4
0.00 ~ 0.50G
5(2)

4.2. Weight Determination

Table 4 Stability score table
Stability
SB (Score)
Less than 60%
060% ~ 65%1
68% ~ 78%2
78% ~ 88%3
88% ~ 95%4
95% ~ 100%
5

In this study, the judgment matrix for the indicators
is constructed using a nine-point scale, as presented inTable 7. This matrix is used to compare the four
indicators in pairs and generate the judgment matrix.
Noted that each element in the matrix should adhere to
the following conditions:𝑥𝑖𝑗 > 0, 𝑥𝑖𝑗 =84

1, (𝑖, 𝑗 = 1,2,3,4)
𝑥𝑗𝑖

(4)

specifications:Intel i5-10400F Processor, 16GB RAM,
Windows 10 64-bit OS, and the NVIDIA GeForce RTX
2080 GPU.

Table 7 Nine-point table of the AHP model
Scaling
1

Definition
Factor 𝑖 is as important as factor 𝑗
Factor 𝑖 is slightly more important thanfactor 𝑗
Factor 𝑖 is significantly more important
than factor 𝑗
Factor 𝑖 is much more important thanfactor 𝑗
Factor 𝑖 is extremely more important
than factor 𝑗
The scale value of the importance offactor 𝑖 over factor 𝑗 is between the above
two adjacent levels

35
7
9
2,4,6,8

Table 9 Confusion matrix for performance evaluation
Input Data
Positive Predictive Negative Predictive
Positive Sample True Positive (TP) False Nagatice (FN)
Negative Sample False Positive (FP) True Negative (TN)5.1. Evaluation Metrics
The experiment result of training and testing areassessed by metrics derived from the confusion matrix,
as shown in Table 9. As mentioned in Section 3, to
simulate the real-world traffic detection scenario, thedataset was deliberately designed to be unbalanced, witha significantly larger number of normal road images(~70%) than road images with traffic signs (~30%) [23].
Therefore, the general metric of accuracy is not
representative to evaluate the performance of detection
system. To ensure an unbiasedanalysis, credible metrics
namely False Alarm Rate (FAR) and Un-Detection Rate
(UND),were used to evaluate the performance. Table 10
illustrates these metrics and corresponding formulas.

Inverse comparison of factor 𝑖 and 𝑗:
𝑥𝑖𝑗 = 1/𝑥𝑗𝑖

Reciprocal of
scaling value

The weight vector can be calculated by arithmetic
mean:
5

𝑥𝑖𝑗
1
𝜔1𝑖 = ∑ 5
(𝑖 = 1,2,3,4)𝑛∑𝑘=1 𝑥𝑘𝑖(5)

𝑗=1

Also, the geometric mean method is expressed asfollows:1𝜔2𝑖 =(∏5𝑗=1 𝑥𝑖𝑗 )5

1 , (𝑖 = 1,2,3,4)
5
5∑𝑘=1 (∏𝑗=1 𝑥𝑖𝑗 )5(6)

Table 10 Evaluation metrics and relevant explanations
Evaluation
Corresponding Formula
Metricsn1
𝑇𝑃 + 𝑇𝑁∑
Accuracy(8)n𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁

The arithmetic mean and the geometric mean are used
to calculate the weight vectors, 𝜔1𝑖 , 𝜔2𝑖 (𝑖 = 1,2,3,4),
and then these vectors are averaged to obtain the final
weight vector, as illustrated in Table 8. After obtaining
the weight for evaluation indicators, the next step is totest for consistency. In this paper, the consistency test of
the judgment matrix yields a value less than 0.1 ,indicating the obtained weight data are valid. The
evaluation model is given as follows:

Score = 0.275𝑅𝑇 + 0.211𝑆𝐵+ 0.342𝐴𝐶 + 0.172𝑆𝐶

1

× 100%
nFalse Alarm Rate
(FAR)

1
𝐹𝑃
∑
× 100%n𝐹𝑃 + 𝑇𝑁(9)

Un-Detection Rate
(UND)

1
𝐹𝑁
∑
× 100%n
𝐹𝑁 + 𝑇𝑃

(10)

1
n

1

(7)
5.2. Experimental Results

Table 8 Weight bar graphIndicator
Weight
RT
0.275
SB
0.211
AC
0.342
SC
0.172

Using the Tsinghua-Tencent 100k dataset, this work
employed ResNet-50, ResNet-101, and ResNet-151
models for training and conducting traffic sign
recognition experiments. In addition, VGG-16 [24] and
GoogLeNet [25] models were implemented forcomparison with the proposed method. Table 11 presents
a performance comparison for these different models,with the ResNet-50 combined with AHP model
achieving the best testing results, boasting an accuracy of
98.01%, a FAR of 0.09%, and an UDN of 1.28%.
Furthermore, this paper selected the optimal parameters

5. Experimental Result and Analysis
This section describes the evaluation metrics utilized
to assess the performance and provides a detailed
analysis of the experimental result. Noted that theexperiment was performed using the following hardware85

of the ResNet models with the best performanceleveraging the AHP evaluation model. By comparing theresults of different parameter configrations in the training
and testing process, the optimal parameters of ResNet
were listed in Table 12.

response time, and system capability, were used for AHP
measurement. The experimental results demostrated that
the ResNet-50 combined with the AHP model achieved
the best performance, with the highest accuracy, lowest
false alarm rate, and lowest un-detection rate. Thesefindings emphasize the efficacy of the proposed method
in improving the reliability and efficiency of traffic signdetection systems, making it a promising solution forreal-world applications.

6. Conclusion
In this study, the method based on image preprocessing techniques and deep learning were applied toenhance the traffic sign detection system. Moreover, thispaper presented a novel AHP model to evaluate theperformance of learning models and determine theoptimal parameter configuration strategy. Four
evaluation indicators, namely accuracy, stability,

Evaluation
Metrics
Training (80%)

Testing (20%)

Accuracy
FAR
UND
Accuracy
FAR
UND

Model Parameters
Convolution Layers
Learning Rate
Split Strategy
Image Enhancement
Colour Processing

Acknowledgements
This work was supported by the Chinese National
Undergraduate Innovation Training Program (No.
202310386056)

Table 11 Experimental results of training and testing
Deep Learning Models
VGG-16
GoogLeNet-22
98.24%
98.89%
0.06%
0.03%
0.87%
0.86%
83.60%
96.62%
2.47%
0.19%
56.73%
2.94%

Table 12 Optimal parameters of ResNets chosen by the AHP modelParameter 1 (Chosen)
Parameter 2
ResNet-101
ResNet-50
Low
Step
Random Split
Classification Split
Image Scale
Brightness Enhancing
RGB
HSV
6.

2.

3.
4.

5.

Parameter 3
ResNet-152
High
Contrast Enhancing

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual
learning for image recognition," in Proceedings of theIEEE conference on computer vision and patternrecognition, 2016, pp. 770-778.
7. N. Zakaria, F. Mohamed, R. Abdelghani, and K. Sundaraj,"Three ResNet Deep Learning Architectures Applied in
Pulmonary Pathologies Classification," presented at the2021 International Conference on Artificial Intelligence
for Cyber Security Systems and Privacy (AI-CSP), 2021.
8. X. Li and L. Rai, "Apple Leaf Disease Identification andClassification using ResNet Models," presented at the2020 IEEE 3rd International Conference on Electronic
Information and Communication Technology (ICEICT),
2020.
9. Y. Lu, J. Lu, S. Zhang, and P. Hall, "Traffic signal
detection and classification in street views using anattention model," Computational Visual Media, vol. 4, no.
3, pp. 253-266, 2018, doi: 10.1007/s41095-018-0116-x.
10. S. B. Wali et al., "Vision-Based Traffic Sign Detection and
Recognition Systems: Current Trends and Challenges,"
Sensors (Basel), vol. 19, no. 9, May 6, 2019, doi:
10.3390/s19092093.

References
1.

ResNet-50+AHP
99.03%
0.01%
0.41%
98.01%
0.09%
1.28%

H. Fleyeh and M. Dougherty, "Road and traffic signdetection and recognition," in Proceedings of the 16th
Mini-EURO Conference and 10th Meeting of EWGT,
2005, pp. 644-653.
M. Pak and S. Kim, "A review of deep learning in image
recognition," presented at the 2017 4th international
conference on computer applications and information
processing technology (CAIPT), 2017.
V. Feng. "An overview of resnet and its variants."(accessed on Jan 6, 2023).
A. Suriya Prakash, D. Vigneshwaran, R. Seenivasaga
Ayyalu, and S. Jayanthi Sree, "Traffic Sign Recognition
using Deep learning for Autonomous Driverless
Vehicles," presented at the 2021 5th International
Conference on Computing Methodologies andCommunication (ICCMC), 2021
X. Changzhen, W. Cong, M. Weixin, and S. Yanmei, "Atraffic sign detection algorithm based on deep
convolutional neural network," in 2016 IEEE International
Conference on Signal and Image Processing (ICSIP),
2016: IEEE, pp. 676-679.

8611. Á. Arcos-García, J. A. Álvarez-García, and L. M. SoriaMorillo, "Evaluation of deep neural networks for traffic
sign detection systems," Neurocomputing, vol. 316, pp.
332-344, 2018, doi: 10.1016/j.neucom.2018.08.009.
12. A. Ruta, Y. Li, and X. Liu, "Detection, tracking andrecognition of traffic signs from video input," in 2008 11th
InternationalIEEE
ConferenceonIntelligent
Transportation Systems, 2008: IEEE, pp. 55-60
13. S. Varun, S. Singh, R. S. Kunte, R. S. Samuel, and B.
Philip, "A road traffic signal recognition system based on
template matching employing tree classifier," inInternational Conference on Computational Intelligenceand Multimedia Applications (ICCIMA 2007), 2007, vol.
3: IEEE, pp. 360-365.
14. H. Fleyeh, "Color detection and segmentation for road andtraffic signs," in IEEE Conference on Cybernetics andIntelligent Systems, 2004., 2004, vol. 2: IEEE, pp. 809814.
15. M. Sridharan and P. Stone, "Towards on-board color
constancy on mobile robots," in First Canadian
Conference on Computer and Robot Vision, 2004.
Proceedings., 2004: IEEE, pp. 130-137.
16. S. Xu, "Robust traffic sign shape recognition using
geometric matching," IET Intelligent Transport Systems,
vol. 3, no. 1, pp. 10-18, 2009.
17. N. Barnes, A. Zelinsky, and L. S. Fletcher, "Real-time
speed sign detection using the radial symmetry detector,"
IEEE Transactions on Intelligent Transportation Systems,
vol. 9, no. 2, pp. 322-332, 2008.
18. S. Maldonado-Bascon, S. Lafuente-Arroyo, P. GilJimenez, H. Gomez-Moreno, and F. Lopez-Ferreras,"Road-Sign Detection and Recognition Based on Support
Vector Machines," IEEE Transactions on Intelligent
Transportation Systems, vol. 8, no. 2, pp. 264-278, 2007,
doi: 10.1109/tits.2007.895311.
19. W.-J. Kuo and C.-C. Lin, "Two-stage road sign detection
and recognition," in 2007 IEEE international conferenceon multimedia and expo, 2007: IEEE, pp. 1427-1430.
20. S. K R, D. M, and V. D. R S, "Traffic Sign Recognition
with Faster RCNN and RPN for Advanced Driver
Assistance Systems," presented at the 2021 5thInternational Conference on Electronics, Communication
and Aerospace Technology (ICECA), 2021.
21. D. Tabernik and D. Skocaj, "Deep Learning for LargeScale Traffic-Sign Detection and Recognition," IEEE
Transactions on Intelligent Transportation Systems, vol.
21,
no.
4,
pp.
1427-1440,
2020,
doi:
10.1109/tits.2019.2913588.
22. H. Cai et al., "Deep Residual Neural Network for Efficient
Traffic Sign Detection," presented at the 2023 28th
International Conference on Artificial ALife and Robotics(ICAROB), 2023.
23. Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu,"Traffic-sign detection and classification in the wild," inProceedings of the IEEE conference on computer visionand pattern recognition, 2016, pp. 2110-2118.
24. J. Yu, X. Ye and Q. Tu, "Traffic Sign Detection and
Recognition in Multiimages Using a Fusion Model With
YOLO and VGG Network," in IEEE Transactions onIntelligent Transportation Systems, vol. 23, no. 9, pp.
16632-16642.

25. J. Xing, and W. Q. Yan. "Traffic sign recognition using
guided image filtering," presented at Geometry and
Vision: First International Symposium, ISGV 2021,
Auckland, New Zealand, January 28-29, 2021. Revised
Selected Papers 1. Springer International Publishi.

Authors Introduction
Mr. Hanlin Cai
He is currently pursuing Bachelor of
Robotics and Intelligent Devices as 4thyear student in the Department ofMaynooth International Engineering
College, Fuzhou University, China. His
research interests are Internet of Things
and applied machine learning.

Mr. Zheng Li
He is currently pursuing Bachelor of
Robotics and Intelligent Devices as 4thyear student in the Department ofMaynooth International Engineering
College, Fuzhou University, China. His
research interests are machine learningand path planning.

Mr. Jiaqi Hu
He is currently pursuing Bachelor of
Robotics and Intelligent Devices as 4thyear student in the Department ofMaynooth International Engineering
College, Fuzhou University, China. His
research interests are machine learningand signal processing.

Dr. Wei Hong Lim
He is an Associate Professor in
Faculty of Engineering at UCSI
University in Malaysia. He received
his
PhD
in
Computational
Intelligence from Universiti Sains
Malaysia in 2014. His researchinterests are optimization andartificial intelligence.

87Dr. Sew Sun Tiang
She is an Assistant Professor in
Faculty of Engineering at UCSI
University in Malaysia. She received
her PhD in Electrical and Electronic
Engineering from Universiti Sains
Malaysia in 2014. Her researchinterests are optimization andantenna design.

Dr.Mastaneh Mokayef
She is an Assistant Professor in the
Faculty of Engineering at UCSI
University in Malaysia. She received
her PhD from the WirelessCommunication Centre Faculty ofElectrical Engineering at University
Technology Malaysia in 2014. Her
research interests include Wirelesscommunications, spectrum sharing.

Dr. Chin Hong Wong
He is a Lecturer at MaynoothInternational Engineering College at
Fuzhou University, China. He
received his PhD in Electrical andElectronic
Engineering
fromUniversiti Sains Malaysia in 2017. His
research
interests
areEnergyharvesting, signals and systems, andcontrol systems.

88