Helmet Detection And Number Plate Recognition Using YOLOV5

Helmet usage is crucial for motorcycle riders to ensure their safety on roads. Moreover, enforcing traffic regulations, such as identifying vehicles without helmets and recognizing their license plates, aids in maintaining road safety and law enforcement. In this project, we propose a robust system for helmet detection and number plate recognition specifically tailored for motorcycles. We utilize the YOLOv5 object detection model to detect motorcycles in images or videos, followed by identifying whether riders are wearing helmets or not. If a rider is detected without a helmet, the system proceeds to recognize the motorcycle's license plate using optical character recognition (OCR). We employ EasyOCR, a Python-based OCR library, to extract text from the license plate images and save the information into a CSV file for further processing. The proposed system provides a comprehensive solution for enhancing road safety and enforcing traffic regulations concerning helmet usage and license plate recognition for motorcycles.