Development of a Traffic Speed Limit Sign Detection System Based on Yolov4 Network

Öz

Recent developments in artificial intelligence technologies have accelerated the transition to smart systems in the automotive industry. By anticipating driving conditions, these technologies enable the prevention of driver-related errors and accidents as well as the provision of crucial information to the driver. In this study, an artificial intelligence-based system is designed to provide information to drivers about speed signs on the road in order to support traffic safety. In this system, Yolov4 model is used to achieve high speed and accuracy levels. After the model training, the model was validated and the test results were found to be 98%.

Anahtar Kelimeler

• Speed Limit Sign
• Object Detection
• Deep Learning
• YoloV4
• Jetson Nano

Kaynakça

1. https://eur-lex.europa.eu/eli/reg/2019/2144/oj, Date of access: 29.09.2022
2. Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2011, July). The German traffic sign recognition benchmark: a multi-class classification competition. In The 2011 international joint conference on neural networks (pp. 1453-1460). IEEE.
3. Dewi, C., Chen, R. C., Liu, Y. T., Liu, Y. S., & Jiang, L. Q. (2020, June). Taiwan stop sign recognition with customize anchor. In Proceedings of the 12th International Conference on Computer Modeling and Simulation (pp. 51-55).
4. Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., & Hu, S. (2016). Traffic-sign detection and classification in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2110-2118).
5. Rajendran, S. P., Shine, L., Pradeep, R., & Vijayaraghavan, S. (2019, July). Real-time traffic sign recognition using YOLOv3 based detector. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
6. Zuo, Z., Yu, K., Zhou, Q., Wang, X., & Li, T. (2017, June). Traffic signs detection based on faster r-cnn. In 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW) (pp. 286-288). IEEE.
7. https://www.nvidia.com/tr-tr/autonomous-machines/embedded-systems/jetson-nano/product-development/, Date of access: 29.09.2022
8. Bochkovskiy A. 2020. Yolo v4, v3 and v2 for Windows and Linux. https://github.com/AlexeyAB/darknet, Date of access: 29.09.2022

9. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
10. Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 390-391).
11. Yu, J., & Zhang, W. (2021). Face mask wearing detection algorithm based on improved YOLO-v4. Sensors, 21(9), 3263.
12. Hu, X., Liu, Y., Zhao, Z., Liu, J., Yang, X., Sun, C., ... & Zhou, C. (2021). Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network. Computers and Electronics in Agriculture, 185, 106135.