A DEEP LEARNING BASED SYSTEM DETECTS HELMET VOILATIONS AND TRIPLE RIDING

The primary goal of this project is to develop an automated system for detecting traffic violations through advanced deep learning models, specifically YOLOv8 and YOLOv11. This system will be capable of detecting and classifying a range of traffic violations, including not wearing helmets, triple riding, phone usage while riding, and wheeling. The project will involve the development of a Python-based backend to process real-time video feeds, ensuring efficient detection of violations as they occur. Additionally, a user-friendly front-end interface will be designed using Streamlit, enabling traffic authorities to easily visualize the results of the detection system and interact with the data. Ultimately, the system aims to improve road safety by providing a reliable, automated solution for real-time traffic violation detection, making the process faster, more accurate, and scalable.