改进YOLOv5s模型，提升输电线路关键部件检测精度与速度
Improved YOLOv5s model for key components detection of power transmission lines
- 优化锚框匹配机制，融合注意力模块和焦点损失函数
- 在输电线路图像上实现98.1% mAP、94.4%召回率和84.8 FPS检测速度
- 适合电力巡检自动化场景，尤其适用于小目标高精度识别
高压输电线路远离道路，导致巡检困难且维护成本高。智能巡检依赖对关键部件的精准检测。针对现有检测精度不足的问题，本文基于YOLOv5s提出改进模型：首先优化k-means聚类中的距离度量以改善锚框匹配；其次在主干网络中引入卷积块注意力模块（CBAM）提升特征表达；最后采用焦点损失函数缓解类别不平衡问题。实验表明，改进模型mAP达98.1%，精度97.5%，召回率94.4%，检测速度达84.8 FPS，优于其他对比模型。
原文摘要 · Abstract (English)
High-voltage transmission lines are located far from the road, resulting in inconvenient inspection work and rising maintenance costs. Intelligent inspection of power transmission lines has become increasingly important. However, subsequent intelligent inspection relies on accurately detecting various key components. Due to the low detection accuracy of key components in transmission line image inspection, this paper proposed an improved object detection model based on the YOLOv5s (You Only Look Once Version 5 Small) model to improve the detection accuracy of key components of transmission lines. According to the characteristics of the power grid inspection image, we first modify the distance measurement in the k-means clustering to improve the anchor matching of the YOLOv5s model. Then, we add the convolutional block attention module (CBAM) attention mechanism to the backbone network to improve accuracy. Finally, we apply the focal loss function to reduce the impact of class imbalance. Our improved method's mAP (mean average precision) reached 98.1%, the precision reached 97.5%, the recall reached 94.4%, and the detection rate reached 84.8 FPS (frames per second). The experimental results show that our improved model improves detection accuracy and has performance advantages over other models.
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