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This project proposes a deep learning–based system for helmet and number plate detection aimed at improving road safety and automating traffic rule enforcement. With the growing number of two-wheeler vehicles and frequent helmet-related violations, manual monitoring has become inefficient. The proposed system uses convolutional neural networks (CNNs) and advanced object detection models such as YOLO or Faster R-CNN to automatically detect motorcycle riders and determine whether they are wearing helmets. If a rider is detected without a helmet, the system further identifies the motorcycle’s number plate and applies optical character recognition (OCR) to extract the registration number for record-keeping or penalty issuance. The model is trained on a diverse dataset containing real-world traffic images and video frames captured under various lighting and environmental conditions, ensuring robustness and accuracy.Overall, this project demonstrates a reliable and scalable solution that leverages deep learning for smart traffic surveillance, automated violation detection, and enhanced road safety management.