MEAG-YOLO: A Novel Approach for the Accurate Detection of Personal Protective Equipment in Substations

Abstract

Timely and accurately detecting personal protective equipment (PPE) usage among workers is essential for substation safety management. However, traditional algorithms encounter difficulties in substations due to issues such as varying target scales, intricate backgrounds, and many model parameters. Therefore, this paper proposes MEAG-YOLO, an enhanced PPE detection model for substations built upon YOLOv8n. First, the model incorporates the Multi-Scale Channel Attention (MSCA) module to improve feature extraction. Second, it newly designs the EC2f structure with one-dimensional convolution to enhance feature fusion efficiency. Additionally, the study optimizes the Path Aggregation Network (PANet) structure to improve feature learning and the fusion of multi-scale targets. Finally, the GhostConv module is integrated to optimize convolution operations and reduce computational complexity. The experimental results show that MEAG-YOLO achieves a 2.4% increase in precision compared to YOLOv8n, with a 7.3% reduction in FLOPs. These findings suggest that MEAG-YOLO is effective in identifying PPE in complex substation scenarios, contributing to the development of smart grid systems.