Published Paper Details:

The detection of helmets and number plates on vehicles is an important task for enhancing road safety and enforcing traffic regulations. In recent years, deep learning-based object detection algorithms have shown promising results for such tasks. This paper proposes a YOLOv5-based system for detecting helmets and number plates in real-time. The YOLOv5 algorithm is a state-of-the-art object detection model that achieves high accuracy and efficiency on various datasets. The proposed system employs a pre-trained YOLOv5 model, fine-tuned on a custom dataset of helmet and number plate images. The system is designed to work in real-time and can process a stream of video frames to detect helmets and number plates on vehicles. The proposed system achieves high accuracy and speed, making it suitable for deployment in traffic monitoring and surveillance systems. Experimental results show that the proposed system outperforms existing state-of-the-art methods for helmet and number plate detection.