Detection method of the seat belt for workers at height based on UAV image and YOLO algorithm

Abstract

In the domain of outdoor construction within the power industry, working at significant heights is common, requiring stringent safety measures. Workers are mandated to wear hard hats and secure themselves with seat belts to prevent potential falls, ensuring their safety and reducing the risk of injuries. Detecting seat belt usage holds immense significance in safety inspections within the power industry. This study introduces detection method of the seat belt for workers at height based on UAV Image and YOLO Algorithm. The YOLOv5 approach involves integrating CSPNet into the Darknet53 backbone, incorporating the Focus layer into CSP-Darknet53, replacing the SPPF block in the SPP model, and implementing the CSPNet strategy in the PANet model. Experimental results demonstrate that the YOLOv5 algorithm achieves an elevated average accuracy of 99.2%, surpassing benchmarks set by FastRcnn, SSD, YOLOX-m, and YOLOv7. It also demonstrates superior adaptability in scenarios involving smaller objects, validated using a UAV-collected dataset of seat belt images. These findings confirm the algorithm's compliance with performance criteria for seat belt detection at power construction sites, making a significant contribution to enhancing safety measures within the power industry's construction practices.