A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition

S Uma; S Prateeksha; V Padmapriya

doi:10.17485/IJST/v17i12.2985

Article

A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition

VIEWS 233
PDF 60

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i12.2985

Year: 2024, Volume: 17, Issue: 12, Pages: 1203-1212

Original Article

A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition

S Uma^1*, S Prateeksha², V Padmapriya³

¹Associate Professor, Department of Computer Applications, B.M.S. College of Engineering, Bangalore, Karnataka, India
²Associate Consultant, CertPro, Bangalore, Karnataka, India
³Assistant Professor, Department of Computer Applications, B.M.S. College of Engineering, Bangalore, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:23 November 2023, Accepted Date:21 February 2024, Published Date:20 March 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The objective of this study is to enhance the accuracy of traffic sign detection and recognition in modern intelligent transport systems, addressing real-time challenges under varying conditions. Methods: A two-phase approach is adopted. The first phase employs the You Only Look Once version 8 (YOLOv8) architecture to efficiently detect traffic signs under real-time conditions, considering variables like adverse weather and obstructions. Subsequently, the second phase employs a sequential convolutional network for precise recognition, utilizing the output from the first phase. This integrated method enhances traffic sign detection and recognition, contributing to road safety and efficient traffic management in complex transportation scenarios. Findings: The YOLOv8 architecture, utilized in Phase 1, demonstrated exceptional performance with a mean Average Precision (mAP) of 0.986 during validation. In Phase 2, the Convolutional Neural Network (CNN)-based recognition model achieved an impressive test accuracy of 98.7% on 463 test images, with a low-test loss of 0.1186, indicating consistent accuracy. The robustness of both models is confirmed by successful testing with three unseen images. YOLOv8 accurately detected and classified these images, while the CNN model correctly recognized them. These findings underscore the effectiveness of the two-phase approach in enhancing traffic sign detection and recognition, with significant implications for improving road safety and traffic management in real-world scenarios. Novelty: The novelty of this approach lies in its seamless integration of YOLOv8 for efficient traffic sign detection and a sequential convolutional network for accurate recognition, offering a significant advancement in addressing real-time challenges and contributing to enhancing road safety and traffic management in an increasingly complex transportation landscape.

Keywords: Traffic sign detection, Traffic sign recognition, Convolutional Neural Networks, YOLOv8, Object detection

References

Global Status Report on Road Safety . 1.World Health Organization. Available from: https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023
Triki N, Karray M, Ksantini M. A Real-Time Traffic Sign Recognition Method Using a New Attention-Based Deep Convolutional Neural Network for Smart Vehicles. Applied Sciences. 2023;13(8):1–21. Available from: https://doi.org/10.3390/app13084793
Lim XR, Lee CP, Lim KM, Ong TS, Alqahtani A, Ali M. Recent Advances in Traffic Sign Recognition: Approaches and Datasets. Sensors. 2023;23(10):1–17. Available from: https://doi.org/10.3390/s23104674
Manual on Uniform Traffic Control Devices (MUTCD) Federal Highway Administration. (n.d.). . Available from: https://mutcd.fhwa.dot.gov/index.htm
Munawar HS. Image and Video Processing for Defect Detection in Key Infrastructure. In: Machine Vision Inspection Systems. (Vol. 1, pp. 159-177) Wiley. 2020.
Temirgaziyeva S, Omarov B. Traffic Sign Recognition With Convolutional Neural Network. Scientific Journal of Astana IT University. 2022;12(12):14–23. Available from: https://doi.org/10.37943/12YZFG6952
Sun Y, Xue B, Zhang M, Yen GG, Lv J. Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification. IEEE Transactions on Cybernetics. 2020;50(9):3840–3854. Available from: https://doi.org/10.1109/TCYB.2020.2983860
GTSRB - German Traffic Sign Recognition Benchmark. Kaggle. Available from: https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
Liu Z, Du J, Tian F, Wen J. MR-CNN: A Multi-Scale Region-Based Convolutional Neural Network for Small Traffic Sign Recognition. IEEE Access. 2019;7:57120–57128. Available from: https://doi.org/10.1109/ACCESS.2019.2913882
Liu C, Li S, Chang F, Wang Y. Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives. IEEE Access. 2019;7:86578–86596. Available from: https://doi.org/10.1109/ACCESS.2019.2924947
Jin Y, Fu Y, Wang W, Guo J, Ren C, Xiang X. Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition. IEEE Access. 2020;8:38931–38940. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9007360
Salimullina AD, Budanov DO. Computer Vision System for Speed Limit Traffic Sign Recognition. In: Computer Vision System for Speed Limit Traffic Sign Recognition. In2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). (pp. 415-418) IEEE. 2022.
Karthika R, Parameswaran L. A Novel Convolutional Neural Network Based Architecture for Object Detection and Recognition with an Application to Traffic Sign Recognition from Road Scenes. Pattern Recognition and Image Analysis. 2022;32(2):351–362. Available from: https://doi.org/10.1134/S1054661822020110
Xie K, Zhang Z, Li B, Kang J, Niyato D, Xie S, et al. Efficient Federated Learning With Spike Neural Networks for Traffic Sign Recognition. IEEE Transactions on Vehicular Technology. 2022;71(9):9980–9992. Available from: https://doi.org/10.1109/TVT.2022.3178808
Nadeem Z, Khan Z, Mir U, Mir UI, Khan S, Nadeem H, et al. Pakistani traffic-sign recognition using transfer learning. Multimedia Tools and Applications . 2022;81:8429–8449. Available from: https://doi.org/10.1007/s11042-022-12177-8
Chen J, Jia K, Chen W, Lv Z, Zhang R. A real-time and high-precision method for small traffic-signs recognition. Neural Computing and Applications. 2022;34(3):2233–2245. Available from: https://doi.org/10.1007/s00521-021-06526-1
Traffic Sign Detection Benchmark. Tsinghua University. Available from: https://cg.cs.tsinghua.edu.cn/traffic-sign/
Zhang Y, Lu Y, Zhu W, Wei X, Wei Z. Traffic sign detection based on multi-scale feature extraction and cascade feature fusion. The Journal of Supercomputing. 2023;79(2):2137–2152. Available from: https://doi.org/10.1007/s11227-022-04670-6
GTSDB Dataset - Test Split. Roboflow. Available from: https://universe.roboflow.com/pfe-fvlva/gtsdb-dat55/browse?queryText=split%3Atest&pageSize=50&startingIndex=0&browseQuery=true
Zhang S, Che S, Liu ZS, Zhang X. A real-time and lightweight traffic sign detection method based on ghost-YOLO. Multimedia Tools and Applications. 2023;82(17):26063–26087. Available from: https://doi.org/10.1007/s11042-023-14342-z
Li Y, Li J, Meng P. Attention-YOLOV4: a real-time and high-accurate traffic sign detection algorithm. Multimedia Tools and Applications. 2023;82(5):7567–7582. Available from: https://doi.org/10.1007/s11042-022-13251-x
Cui Y, Guo D, Yuan H, Gu H, Tang H. Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection. Applied Sciences. 2024;14(2):1–15. Available from: https://doi.org/10.3390/app14020555
Terven J, Córdova-Esparza DM, Romero-González JA. A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction. 2023;5(4):1680–1716. Available from: https://doi.org/10.3390/make5040083
Jiang P, Ergu D, Liu F, Cai Y, Ma B. A Review of Yolo Algorithm Developments. Procedia Computer Science. 2022;199:1066–1073. Available from: https://doi.org/10.1016/j.procs.2022.01.135
Aboah A, Wang B, Bagci U, Adu-Gyamfi Y. Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8. 2023. Available from: https://doi.org/10.48550/arXiv.2304.08256

Copyright

© 2024 Uma et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)