AN OPTIMIZED YOLOV10 FRAMEWORK FOR DUAL DETECTION OF SAFETY HELMETS AND LICENSE PLATES

Abstract

This research uses the most recent YOLOv10 object detection architecture to demonstrate a sophisticated computer vision system for real-time safety helmet detection and license plate recognition. The main goal is to improve vehicle monitoring and workplace safety by automatically recognising people who are not wearing safety helmets in industrial zones and recording license plates for regulatory and surveillance purposes. Efficient multi-object identification in difficult situations is made possible by YOLOv10, which is renowned for its exceptional speed and accuracy. To ensure reliable performance, the system is trained using annotated datasets that include a variety of helmet types and car plates under various circumstances. Construction workers' risk of suffering head injuries in highaltitude falls can be significantly decreased by donning safety helmets. This study suggests an enhanced safety helmet detection method based on YOLOv10 to solve the low detection accuracy of current algorithms for small objects and complicated settings in different situations.