YOLO-M-Based Model for Detecting Safety Helmet Wearing At Construction Sites

Ensuring safety on construction sites continues to be a major challenge, highlighting the need for intelligent and automated monitoring systems. This research presents a smart helmet detection framework based on advanced computer vision techniques, designed to enforce safety measures in real-time. The paper systematically evaluates multiple cutting-edge object detection models—such as YOLOv5s, YOLOv5-M, YOLOv3, YOLOv4, YOLOv8, YOLOv5 integrated with GhostCNN, SSD, RetinaNet, and Faster R-CNN. These models are assessed based on detection accuracy, processing speed, and hardware efficiency to determine their viability for safety enforcement tasks. The system primarily aims to safeguard construction personnel while also assisting site supervisors by enhancing resource management and surveillance. Experimental results indicate that the YOLOv5-GhostCNN architecture achieves a remarkable performance, attaining a mean Average Precision (mAP) exceeding 97%, highlighting its capability in critical safety applications. This work advances the goal of safer construction environments by promoting effective use of AI-based safety monitoring.