Helmet Detection Using Yolo Shift Invariant Technique

Abstract

The abstract for the project describes a computer vision system for detecting helmets worn by motorcyclists in real-time using You Only Look Once (YOLO) object detection method. The system is designed to perform in various lighting and surrounding circumstances and is capable of detecting helmets even when the wearer is in motion or there are shifts in the camera's perspective. The system is trained on an enormous dataset of annotated helmet photos and can accurately detect helmets with a high degree of precision and recall. The project demonstrates the potential of YOLO and deep learning techniques to improve road safety by automatically detecting and alerting riders who are not wearing helmets, helping to reduce the number of motorcycle-related accidents and fatalities.