Traffic Sign Sensing: A Deep Learning approach for enhanced Road Safety

Abstract

Abstract There exists paramount importance of traffic signs in ensuring road safety and efficiency, emphasizing their role in communicating vital informa- tion and regulations, transcending language barriers, and aiding drivers in navigating roads and avoiding potential dangers. In light of this significance, our study focuses on harnessing object detection technology, specifically ap- plied to traffic signs, with the goal of accident prevention and enhanced road safety. The dataset employed for our research is Road Sign Detection, and this paper has implemented the latest YOLOv8 versions and YOLO-NAS-l for object detection. Notably, this work achieved impressive mAP50 values, with YOLOv8m at 94.3%, YOLOv8x at 91.9%, YOLOv8l at 92%, YOLOv8s at 94.9%, and YOLOv8n at 95.3%. However, the best results were obtained by YOLO-NAS-l, with an outstanding mAP50 score of 95.72%. In addition to these individual results, this work conducted a comprehensive comparative study, demonstrating that our model leads to an 8% increase in performance over existing approaches. This paper is of great significance as it addresses a critical aspect of road safety and presents a cutting-edge solution that not only outperforms previous models but also sets a new standard for the industry. This research holds the potential to substantially reduce road accidents and enhance overall road safety, making it an invaluable contribution to the field of computer vision and its practical applications in the real world.