HELMET DETECTION FOR MVD

Abstract

Helmet detection plays a crucial role in ensuring the safety of motorcyclists. Helmet detection system using the YOLOv5 architecture and integrate it into a Django web application is proposed. The system aims to automatically detect the presence of helmets in images or real time video streams. The YOLOv5 model trained on a dataset consisting of helmet and non helmet images, utilizing transfer learning for improved performance. The trained model is serialized and integrated into the Django application, allowing for seamless inference and prediction of helmet detection. API endpoints are defined to receive image inputs and return the detection results. The system includes data preprocessing steps to handle incoming images and optimize them for the model's input requirements. The deployment and integration of the model into the Django framework enable real-time helmet detection and provide a user-friendly interface for users to interact with the system. Extensive testing and evaluation demonstrate the effectiveness and accuracy of the helmet detection system. The developed solution has the potential to contribute significantly to promoting helmet usage and enhancing road safety for motorcyclists.