REAL-TIME DETECTION OF TRAFFIC SIGNS WITH YOLO ALGORITHMS

Abstract

The number of electric vehicles is increasing day by day. The biggest reason for the increase in electric vehicles is their autonomous or semi-autonomous use feature. Autonomous or semi-autonomous driving; It is the movement of the vehicle with the data coming from the sensors, cameras, and sensors around the vehicle. The majority of traffic accidents are caused by driver errors. The most important of these mistakes is not obeying traffic rules. Autonomous or semi-autonomous driving largely prevents driver-related traffic accidents. The biggest problem of autonomous vehicles is the difficulties in detecting traffic signs in real-time. The locations, shapes, and scales of traffic signs are very different. Traffic signs are difficult to detect in real-world conditions due to their similarity to other objects. The study carried out real-time detection of traffic signs. For this purpose, images were taken from the camera placed inside the vehicle. A data set was created with these images. The more real environment images the data set consists of, the more accurate the real-time detection process increases. In this study, 8931 traffic sign images were taken from real environments. These images were taken from different locations, different lighting levels, and different distances. In addition, the number of data was increased to 78895 by adding grayscale, adding slope, blurring, adding variability, adding noise, changing image brightness, changing colour vividness, changing perspective, resizing, and positioning the images. With this study, the data set was adapted to the real environment. The created data set was used in 3 different versions of YOLOv5 architecture, YOLOv6, YOLOv7 and YOLOv8 architectures. As a result of the study, the highest accuracy was found to be 99.60%, F1-Score was 0.962 and mAP@.5 value was 0.993 in YOLOv8 architecture.

Keywords

• Deep Learning
• Real-Time Detection
• Traffic Signs
• YOLO

References

1. J. Webb, C. Wilson, and T. Kularatne, "Will people accept shared autonomous electric vehicles. A survey before and after receipt of the costs and benefits," Economic Analysis and Policy, vol. 61, pp. 118–135, 2019. https://doi.org/10.1016/j.eap.2018.12.004.
2. A. Moorthy, R. De Kleine, G. Keoleian, J. Good, and G. Lewis, "Shared autonomous vehicles as a sustainable solution to the last mile problem: A case study of Ann Arbor-Detroit area," SAE International Journal of Passenger Cars-Electronic and Electrical Systems, vol. 10(2), 2017. https://doi.org/10.4271/2017-01-1276.
3. A. Faisal, T. Yigitcanlar, G. Currie, S. Journal, and L. Use, "Understanding autonomous vehicles Linked references are available on JSTOR for this article: Understanding autonomous vehicles: A systematic literature re- view on capability, impact, planning and policy," Journal of Transport and Land Use, vol. 12(1), pp. 45–72, 2019. https://doi.org/10.5198/jtlu.2019.1405
4. H. Fleyeh, "Color detection and segmentation for road and traffic signs," IEEE Conference on Cybernetics and Intelligent Systems, vol. 2, pp. 809-814, 2004. https://doi.org/0000-0002-1429-2345.
5. S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel, "Detection of traffic signs in realworld images: The German traffic sign detection benchmark," The 2013 International Joint Conference on Neural Networks, pp. 1-8, 2013. https://doi.org/10.3390/app11073061.
6. J. Liu, and X. Wang, "Tomato diseases and pests detection based on improved Yolov3 convolutional neural network," Frontiers in Plant Science, vol. 11, pp. 898, 2020. https://doi.org/10.3389/fpls.2020.00898.
7. S. Tan, G. Lu, Z. Jiang, and L. Huang, "Improved Yolov5 network model and application in safety helmet detection," 2021 IEEE International Conference on Intelligence and Safety for Robotics, pp. 330-333, 2021. https://doi.org/10.1109/ISR50024.2021.9419561
8. F. Zhou, H. Zhao, and Z. Nie, "Safety helmet detection based on Yolov5," IEEE International Conference on Power Electronics Computer Applications, pp. 6-11, 2021. https://doi.org/10.3390/s22249843.

9. F. N. Ortataş, and E. Çetin, "Traffic Sign Recognition and the Application of Simulation Using Machine Learning in Electric and Autonomous Vehicles," El-Cezerî Journal of Science and Engineering, vol. 8 (3), pp. 1081-1092, 2021. https://doi.org/10.31202/ecjse.867733.
10. F. Mehdi, and G. Sedigheh, "Traffic Road Sign and Classification," Majlesi Journal of Electrical Engineering, vol. 4 (23), pp. 54- 62, 2021.
11. L. Zeng, B. Sun, and D. Zhu, "Underwater target detection based on Faster R-CNN and adversarial occlusion network," Engineering Applications of Artificial Intelligence, vol. 100, pp. 104190, 2021. https://doi.org/10.1016/j.engappai.2021.104190
12. F. Shao, X. Wang, F. Meng, J. Zhu, D. Wang, and J. Dai, "Improved faster R-CNN traffic sign detection based on a second region of interest and highly possible regions proposal network," Sensors, vol. 19 (10), pp. 2288, 2019. https://doi.org/10.3390/s19102288.
13. J. Zhang, M. Huang, X. Jin, and X. Li, "A real-time Chinese traffic sign detection algorithm based on modified Yolov2," Algorithms, vol. 10 (4), pp. 127, 2017. https://doi.org/10.3390/a10040127.
14. C. Liu, S. Li, F. Chang, and Y. Wang, "Machine Vision Based Traffic Sign Detection Methods: Review," Analyses and Perspectives, IEEE Access, vol. 7(1), pp. 86578-86596, 2019. https://doi.org/10.1109/Access.2019.2924947
15. O. Belghaouti, W. Handouzi, and M. Tabaa, "Improved traffic sign recognition using deep ConvNet architecture," Procedia Computer Science, vol. 177, pp. 468–473, 2020. https://doi.org/10.1016/j.procs.2020.10.064
16. D. Tabernik, and D. Skočaj, "Deep learning for large-scale traffic-sign detection and recognition," IEEE transactions on intelligent transportation systems, vol. 21 (4), pp. 1427-1440, 2019. https://doi.org/10.1109/TITS.2019.2913588
17. J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, "Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition," Neural networks, vol. 32, pp. 323-332, 2022. https://doi.org/10.1016/j.neunet.2012.02.016.
18. J. Kim, and M. Lee, "Robust Lane Detection Based On Convolutional Neural Network and Random Sample Consensus," Neural Information Processing. 2014. https://doi.org/10.1007/978-3-319-12637-1_3.
19. R. Qian, B. Zhang, Y. Yue, Z. Wang, an F. Coenen, "Robust Chinese traffic sign detection and recognition with deep convolutional neural network," 11th International Conference on Natural Computation, pp. 791-796, 2015. https://doi.org/10.1109/ICNC.2015.7378104.
20. A. Arcos-Garcia, J. A. Alvarez-Garcia, and L. M. Soria-Morillo, "Evaluation of deep neural networks for traffic sign detection systems," Neurocomputing, vol. 316, pp. 332-344, 2018. https://doi.org/10.1016/j.neucom.2018.07.072.
21. S. Hussain, M. Abualkibash, and S. Tout, "A survey of traffic sign recognition systems based on convolutional neural networks," IEEE International Conference on Electro/Information Technology, pp. 0570-0573, 2018. https://doi.org/10.1109/EIT.2018.8399772.
22. M. E. I. Malaainine, H. Lechgar, H. Rhinane, "YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking," Journal of Geographic Information System, vol. 13, pp. 395-409, 2021. https://doi.org/0.4236/jgis.2021.134022.
23. D. Dluznevskij, P. Stefanovic, S. Ramanauskaite, "Investigation of YOLOv5 Efficiency in iPhone Supported Systems," Baltic J. Modern Computing, vol. 9, pp. 333–344, 2021. https://doi.org/10.22364/bjmc.2021.9.3.07
24. B. R. Chang, H. Tsai, C. Hsieh, "Accelerating the Response of Self-Driving Control by Using Rapid Object Detection and Steering Angle Prediction," Electronics, vol. 12, pp. 2161, 2023. https://doi.org/10.3390/electronics12102161
25. H. Herfandi, O. S. Sitanggang, M. R. A. Nasution, H. N, Y. M. Jang, "Real-Time Patient Indoor Health Monitoring and Location Tracking with Optical Camera Communications on the Internet of Medical Things," Appl. Sci. vol. 14, pp. 1153, 2024. https://doi.org/10.3390/app14031153
26. B. Xing, W. Wang, J. Qian, C. Pan, Q. Le,"A Lightweight Model for Real-Time Monitoring of Ships," Electronics, vol. 12, pp. 3804, 2023. https://doi.org/10.3390/electronics12183804.
27. Y. Zhu, and W. Yan, "Traffic sign recognition based on deep learning," Multimedia Tools and Applications, vol. 81(13), pp. 17779-17791, 2022. https://doi.org/10.1007/s11042-022-12163-0
28. H. Wan, L. Gao, M. Su, Q. You, H. Qu, and Q. Sun, "A novel neural network model for traffic sign detection and recognition under extreme conditions," Journal of Sensors, 2021. https://doi.org/10.1155/2021/9984787.
29. E. Lu, H. C. M. Gozdzikiewicz, K. H. Chang, and J. M. Ciou, "A hierarchical approach for traffic sign recognition based on shape detection and image classification," Sensors, vol. 22 (13), pp. 4768, 2022. https://doi.org/10.3390/s22134768.
30. L. Yi, L. Jinguo, Z. Yongjie, and M. Ping, "Detection of self-explosive insulators in aerial images based on improved Yolov4," In Journal of Physics: Conference Series, vol. 2320 (1), pp. 012025, 2022. https://doi.org/10.1088/1742-6596/2320/1/012025
31. L. Jiang, H. Liu, H. Zhu, and G. Zhang, "Improved Yolov5 with balanced feature pyramid and attention module for traffic sign detection," MATEC Web of Conferences, pp. 355, 2022. https://doi.org/10.1051/matecconf/202235503023
32. A. Aggar, M. Zaiter, "Iraqi traffic signs detection based on Yolov5," International Conference on Advanced Computer Applications, pp. 5-9, 2021. https://doi.org/10.2991/978-94-6463-034-3_72
33. G. Çınarer, "Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm," Düzce University Journal of Science & Technology, vol. 12, pp. 219-229, 2022. https://doi.org/10.29130/dubited.1214901.
34. A. Jahongir, and Ö. A. Ahmet, "A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways," Advanced Engineering Informatics, vol. 50, pp. 101393, 2021. https://doi.org/10.1016/j.aei.2021.101393 ,
35. R. B. Rodriguez, C. M. Carlos, O. O. V. Villegas, V. G. C. Sanchez, and H. J. O. Dominguez, "Mexican traffic sign detection and classification using deep learning," Expert Systems With Applications, vol. 202, pp. 117247, 2022. https://doi.org/10.1016/j.eswa.2022.117247.
36. B. Ren, J. Zhang, T. Wang "A Hybrid Feature Fusion Traffic Sign Detection Algorithm Based on YOLOv7," Computer, Materials and Continua, vol. 80, pp. 1425-1440, 2024. https://doi.org/10.32604/cmc.2024.052667
37. C. Hsieh, C. Hsu, W. Huang, "A two-stage road design detection and text recognition system based on YOLOv7," Internet of Things, vol. 27, pp. 101330, 2024. https://doi.org/10.1016/j.iot.2024.101330
38. P. Kappusamy, M. Sanjay, P. V. Deepshree, C. Iwendi, "Traffic Sign Reorganization for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module," Computer, Materials and Continua, vol. 77, pp. 445-466, 2023. https://doi.org/10.32604/cmc.2023.042675
39. Q. Yu, X. Xu, P. Xia, S. Xu, H. Wang, A. Rodic, P. B. Petrovic, "YOLOv7-Tiny road target detection algorithm based on attention mechanism," Procedia Computer Science, vol. 250, pp. 95-110, 2024. https://doi.org/10.1016/j.procs.2024.11.014
40. R. Zhao, S. H. Tang, J. Shen, E. E. B. Supeni, S. A. Rahim "Enhancing autonomous driving safety: A robust traffic sign detection and recognition model TSD-YOLO," Signal Processing, vol. 225, pp. 109619, 2024. https://doi.org/10.1016/j.sigpro.2024.109619
41. B. Ji, J. Xu, Y. Liu, P. Fan, M. Wang, "Improved YOLOv8 for small traffic sign detection under complex environmental conditions," Franklin Open, vol. 8, pp. 100167, 2024. https://doi.org/10.1016/j.fraope.2024.100167
42. L. Zhang, K. Yang, Y. Han, J. Li, W. Wei, H. Tan, P. Yu, K. Zhang, X. Yang, "TSD-EDR: A lightweight real-time detection transformer of traffic sign detection for long-range perception of autonomous driving," Engineering Applications of Artificial Intelligence, vol. 139, pp. 109536, 2025. https://doi.org/10.1016/j.engappai.2024.109536
43. H. S. G. Yaamini, K. J. Swathi, N. Manohor, G. A. Kumar, "Lane and traffic sign detection for autonomous vehicle: addressing challenges on Indian road conditions," MethodsX, vol. 14, pp. 103178, 2025. https://doi.org/10.1016/j.mex.2025.103178
44. Y. Han, F. Wang, W. Wang, X. Zhang, X. Li, "EDN-YOLO: Multi-scale traffic sign detection method in complex scenes," Digital Signal Processing, vol. 153, pp. 104615, 2024 https://doi.org/10.1016/j.dsp.2024.104615.
45. F. Mercaldoa, F. Martinellib, A. Santonea, "Real-Time Road Sign Localisation through Object Detection," Procedia Computer Science, vol. 246, pp. 30–37. https://doi.org/10.1016/j.procs.2024.09.225.
46. Y. Sun, X. Li, D. Zhao, Q. Wang, "Evolving traffic sign detection via multi-scale feature enhancement, reconstruction and fusion," Digital Signal Processing, vol. 160, pp. 105028, 2025 https://doi.org/10.1016/j.dsp.2025.105028.