提升电力工人安全装备检测精度，应对遮挡与复杂环境挑战
Enhanced PEC-YOLO for Detecting Improper Safety Gear Wearing Among Power Line Workers
- 融合PConv与EMA注意力机制增强特征提取
- 相比YOLOv8s准确率提升2.7%，参数减少42.58%
- 适合电力巡检等高危场景的实时智能监控
为应对复杂输电线路环境中因目标遮挡和显著差异带来的高风险，本文提出一种增强型PEC-YOLO目标检测算法。该方法结合深度感知与多尺度特征融合，利用PConv和EMA注意力机制提升特征提取效率并降低模型复杂度。CPCA注意力机制被引入SPPF模块，增强模型对关键信息的关注能力，提高在复杂条件下的检测精度。此外，采用BiFPN颈部结构优化低层与高层特征的利用，通过自适应融合与上下文感知机制强化特征表示。实验结果表明，所提PEC-YOLO相较YOLOv8s检测准确率提升2.7%，模型参数减少42.58%。在相同条件下，其检测速度优于其他模型，满足施工现场对安全装备检测的严苛精度要求。本研究推动了高危环境下高效精准智能监控系统的发展。
原文摘要 · Abstract (English)
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.
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