TR-TRVD: Triple Riders - Traffic Rule Violation Detection using YOLOv8-BBIA for Intelligent Transportation System

Abstract

To restraints the rate of fatality due to accident, a powerful and adequate implementation of traffic rules and continuous monitoring is required. Traffic Rule Violation Detection (TRVD) system aims to identify the traffic rule violation – triple riding and ensures that the rule must be followed 24*7 without any human intervention. To detect the traffic rule violation, deep learning based single shot detection algorithm is utilized. YOLO (You Only Look Once) algorithm used for detection of two wheeler and number of persons riding on a motorcycle, the system detect and classify a person is following a traffic rule strictly or not. The suggested method trains a model on a datasets that combines custom images with publicly available datasets. This approach is very effective at accurately detecting traffic rule violations related to triple riding, whether it's a single rider or multiple riders on the bike.Furthermore, to address the issue of class imbalance, data augmentation techniques were utilizedto increase the variation in training data. This strengthen the model's effectiveness in applying to practical scenarios. Different YOLO family algorithm has been utilized for development of detection model. The YOLOv8 model was tested on a total of 80 images and  detection accuracy exhibited an F1 score, precision and mAP@50 of 76.4%, 73.5% and 81.6% respectively for all classes. We manually tested triple riding traffic rule violation using our proposed algorithm and found that the system gives 92.7% of accuracy. These findings highlight the potential of proposed model, thus fostering safer motorcycling practices.