The Application of YOLOv10 Model Integrated with RepViT in Safety Protective Equipment Detection for Power Maintenance Workers

With the increasing emphasis on safety management in industrial settings, ensuring that power maintenance workers wear proper protective equipment has become a critical concern. Traditional safety monitoring methods often rely heavily on manual intervention, leading to inefficiencies and susceptibility to human error. To address these challenges, computer vision and deep learning technologies provide an effective solution for automated safety detection. This study employs an improved YOLO (You Only Look Once) algorithm to enhance detection accuracy while optimizing efficiency and real-time performance. The proposed approach meets the safety management requirements of power maintenance operations by enabling intelligent monitoring of workers' protective equipment. By automating safety inspections, the system significantly reduces the risk of accidents and enhances worker safety, contributing to a more secure and efficient maintenance environment.