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

TL;DR

This paper introduces an enhanced PEC-YOLO algorithm that improves safety gear detection accuracy and efficiency in power line environments by integrating advanced attention mechanisms and feature fusion techniques.

Contribution

The paper presents a novel PEC-YOLO model with integrated attention mechanisms and a BiFPN architecture, achieving higher accuracy and lower complexity than existing models.

Findings

2.7% improvement in detection accuracy over YOLOv8s

42.58% reduction in model parameters

Faster detection speed with improved accuracy

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.