CLEAR: An Efficient Traffic Sign Recognition Technique for Cyber-Physical Transportation Systems

Abstract

In this modern era, the popularity of autonomous systems has increased manifold because they are replacing people in various jobs and are expected to become the backbone of modern society. Specifically, self-driving vehicles and robots are gaining popularity in a wide range of applications. However, traffic sign detection, a critical component of intelligent transportation systems, remains quite challenging since it needs to be done quickly, with high precision, and with high dependability. A fast, real-time, robust automatic traffic sign detection and recognition can support and relieve the driver and significantly increase driving safety and comfort. Out of many algorithms and frameworks available for traffic sign identification, i.e., object detection, one of the most popular ones is You Only Look Once (YOLO) since it provides accurate results with minimal background errors in most real-time processing tasks and has excellent learning capabilities. Motivated by this, this research article provides a novel framework, CLEAR, for traffic sign identification in adverse climate conditions using YOLOv5. We compare different models’ speed, accuracy, and other metrics on a Traffic Sign Dataset obtained from the Open Image Dataset V6. Experimental results demonstrated that the proposed CLEAR model achieved the best performance with a Mean Average Precision of 0.73392 and Recall of 0.74194 compared to the existing schemes.