IoT Based Two-Wheelers Traffic Violation Detection System Using YOLOv7

Addressing traffic violations, particularly concerning motorcycle safety, remains a pressing issue in many urban areas. This research proposes a novel approach for automating the detection of motorcycle riders without helmets and those engaging in triple riding, and instances of overspeeding or speed violations. By leveraging state-of-the-art computer vision techniques and deep learning algorithms, the system analyzes both image and video inputs in real-time to identify and classify traffic violations accurately. The integration of the You Only Look Once (YOLO) algorithm ensures efficient object detection, enabling prompt intervention by traffic authorities. Through the utilization of diverse datasets and advanced machine learning models, this research aims to enhance the effectiveness of traffic law enforcement and promote safer road behavior.