Helmet Detection System Using Yolo V2

The risk of head injuries. The methodology The increasing concern for safety in a variety of work and leisure environments mandates the production of robust and efficient helmet detection systems. This study offers a fresh method for helmet detection, utilizing the (You Only Look Once version 2) YOLOv2 algorithm. The proposed system leverages the capabilities of YOLOv2 for real-time object detection, providing a proactive solution to enforce helmet-wearing regulations and mitigate impacts that encompass gathering of data, pre-processing, designing of the model architecture, training, and assessment. Results from experiments show the YOLO has achieved the greatest mAP success rate of 96.72%. How well the suggested approach works in precisely detecting helmets in diverse environments and scenarios. Furthermore, discussions on implementation considerations, challenges, and future research directions are provided, highlighting the potential impact of the helmet detection system on enhancing safety across industries and activities.