Traffic Vehicle Monitoring Using CNN - Tapa blanda

Sinopsis

This project presents an advanced, AI-powered surveillance system specifically engineered to enhance road safety and enforce traffic regulations concerning two wheeler riders. The system autonomously detects helmet violations and illegal triple-riding-two of the most prevalent and hazardous traffic infractions involving motorcycles-using a synergy of real-time video analysis and deep learning techniques. At the core of the detection pipeline is YOLOv8, a state-of-the-art object detection model acclaimed for its high-speed inference and remarkable accuracy, enabling it to identify motorcyclists, count riders, and determine helmet usage with precision in live video feeds. The visual data is processed using OpenCV, which captures and refines each frame for effective object detection. In tandem with this, the system incorporates an Automatic Number Plate Recognition (ANPR) module, powered by Easy OCR, to accurately extract license plate information from detected vehicles once a violation is confirmed.

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