Efficient Traffic Rule Enforcement Framework using YOLOv8-Based Object Detection
With the increasing number of vehicles, urbanization and the constant rise in road usage, traffic violations have become hugely problematic in today's transportation situations. Some of the most common dangerous driving behaviors that lead to road accidents and traffic delays are as follows: Not wearing a helmet, running a red light, and breaking lanes, breaking seatbelt, using a cell phone while driving and triple riding. Maintaining consistent observation, precise detection, scalability, and speedy identification of traffic infractions in complex road situations are all challenges faced by current traffic violation monitoring methods. Factors such as high traffic levels, uneven lighting, environmental interference, and requiring human supervision limit the effectiveness of current monitoring methods. Therefore, it becomes essential to have a sophisticated automated system that can efficiently do real-time traffic infraction analysis. The proposed study utilizes a novel Traffic violations identification method based on YOLOv8 to detect many traffic violations in the surveillance photos and videos. To achieve the system execution, a Traffic Rule-net Dataset was developed from a set of traffic data collected from different scenarios of city transport, highways and crossroads. The quality of the features and the robustness of the suggested model were enhanced using a number of data pre-processing techniques, including normalization, image size modification, data augmentation, and filtering. The new framework was applicable in all environmental conditions, allowing for efficient object localization and classification of different breaches. From the experimental evaluation it is clear that there was a reduction in false detection, performance, and detection efficiency. Infrancements of the traffic rules may be easily and efficiently detected for traffic control through the use of intelligent monitoring.