Enhanced PEC-YOLO for Detecting Improper Safety Gear Wearing Among Power Line Workers

TL;DR

This work tackles the challenge of detecting improper safety gear wearing among power-line workers in complex environments with occlusion and wide target variance. It proposes PEC-YOLO, an enhanced YOLOv8-based detector that integrates PConv and EMA attention in a C2F_Faster_EMA backbone, CPCA-enhanced SPPF, and a BiFPN-based neck to improve multi-scale feature fusion while reducing model size. Empirical results show PEC-YOLO achieves a $2.7\%$ improvement in $mAP$ over YOLOv8s, with a $42.58\%$ reduction in parameters and faster detection speed; ablations confirm the contributions of each module. The approach enables accurate, efficient safety-gear monitoring suitable for edge deployment, supporting safer operation in hazardous power-site environments.

Abstract

To address the high risks associated with improper use of safety gear in complex power line environments, where target occlusion and large variance are prevalent, this paper proposes an enhanced PEC-YOLO object detection algorithm. The method integrates deep perception with multi-scale feature fusion, utilizing PConv and EMA attention mechanisms to enhance feature extraction efficiency and minimize model complexity. The CPCA attention mechanism is incorporated into the SPPF module, improving the model's ability to focus on critical information and enhance detection accuracy, particularly in challenging conditions. Furthermore, the introduction of the BiFPN neck architecture optimizes the utilization of low-level and high-level features, enhancing feature representation through adaptive fusion and context-aware mechanism. Experimental results demonstrate that the proposed PEC-YOLO achieves a 2.7% improvement in detection accuracy compared to YOLOv8s, while reducing model parameters by 42.58%. Under identical conditions, PEC-YOLO outperforms other models in detection speed, meeting the stringent accuracy requirements for safety gear detection in construction sites. This study contributes to the development of efficient and accurate intelligent monitoring systems for ensuring worker safety in hazardous environments.

Figures (10)

• Figure 1: YOLOv8 network structure
• Figure 2: PEC-YOLO Network Structure
• Figure 3: Working Principle of PConv
• Figure 4: Structure of the EMA Attention Module
• Figure 5: Bottleneck Structure Diagram