SEATBELT DETECTION IN TRAFFIC SYSTEM USING AN IMPROVED YOLOv5

Abstract

Seat belt use is essential for avoiding fatalities and serious injuries in car accidents. Due to the challenge of identifying vehicles in images of a traffic scene that are affected by complicated illumination, the typical seat belt identification algorithm has low accuracy for determining the driver's seatbelt status. We proposed seat belt detection in a traffic management system utilizing an improved YOLOv5 algorithm developed by combining YOLOv5 with brightness augmentation. This procedure is used to improve existing algorithms for seat belt detection. The framework incorporates image enhancement, region proposal generation, depth feature extraction, target recognition, and detection into a convolutional neural network model, which significantly boosts training efficiency and detection accuracy. To examine the performance of the upgraded YOLOv5 detection algorithm, a benchmark dataset known as the Yawning Detection Dataset was gathered. The examination focuses on identifying the state of a safety belt between two classes: "seatbelt" and "non-seatbelt". The results demonstrate a high level of accuracy, with a mean average precision (mAP) of 96%, Precision of 99%, Recall of 99%, and a true positive score of 95.7%, indicating the system’s effectiveness in identifying the safety belt status.