A Deep Learning based System to Detect Triple Riding and Helmet Violations

Real-time monitoring systems use surveillance videos to automatically detect motorcycle helmet requirements and triple-riding violations, which protect road safety. Deep learning methods currently show practical worth for addressing surveillance system constraints because they have developed superior capabilities in object detection and classification. The models deliver poor results repeatedly because they are limited by low-resolution video, together with adverse weather conditions, as well as problems from occlusions and deficient illumination conditions. The issue of recognizing multiple individuals riding together on one motorcycle requires better solution methods because it remains unresolved. YOLOv8 operates in real-time to detect both helmet violations and multiple riders on vehicles using CCTV video and webcam feeds. The detection system reaches a 95% accuracy level, thereby establishing itself as an effective monitoring method in automated traffic control. A YOLOv8 detection model serves for detecting triple riders, along with YOLOv8-based detection of helmet violations. The model has tailored detection skills since its training with helmet-specific data enables it to monitor properly non-helmeted riders. The system supplies capabilities for processing video and image data, which helps it create visual violation marks for practical realworld applications. Quality evaluation of the system happens through precision and recall metrics, which measure its reliable operation functions. The method utilizes publicly available data and self-collected information to solve illumination conditions and issues that affect detection systems. The evolution of traffic rule enforcement systems has improved thanks to the resolution of implementation problems. The research application proves how deep learning transforms surveillance systems, which advances road safety through system improvements. Better traffic management possibilities, combined with improved efficiency and innovation, enable wider expansion of safety programs in urban areas.