基于改进YOLOv7的输电线路机械外破隐患目标检测方法

王彦海; 郭宸昕; 吴德强

王彦海,郭宸昕,吴德强.基于改进YOLOv7的输电线路机械外破隐患目标检测方法[J].电测与仪表,2026,63(1):64-71.
WANG Yanhai,GUO Chenxin,WU Deqiang.Hidden target detection method for mechanical external damage of transmission line based on improved YOLOv7[J].Electrical Measurement & Instrumentation,2026,63(1):64-71.

基于改进YOLOv7的输电线路机械外破隐患目标检测方法

Hidden target detection method for mechanical external damage of transmission line based on improved YOLOv7

DOI：10.19753/j.issn1001-1390.2026.01.007

中文关键词: YOLOv7 输电线路机械外破 Swin Transformer注意力机制 Mish激活函数 Focal-EIOU损失函数

英文关键词:YOLOv7, transmission line, mechanical external damage, Swin Transformer attention mechanism, Mish activation function, Focal-EIOU loss function

基金项目:国家自然科学基金资助项目（52079070）

作者中文名	作者英文名	单位
王彦海	WANG Yanhai	1.三峡大学电气与新能源学院, 湖北宜昌 443002; 2.湖北省输电线路工程技术研究中心, 湖北宜昌 443002
郭宸昕	GUO Chenxin	1.三峡大学电气与新能源学院, 湖北宜昌 443002; 2.湖北省输电线路工程技术研究中心, 湖北宜昌 443002
吴德强	WU Deqiang	1.三峡大学电气与新能源学院, 湖北宜昌 443002; 2.湖北省输电线路工程技术研究中心, 湖北宜昌 443002

摘要点击次数: 49

中文摘要:

针对背景复杂、尺度变化较大、被遮挡情况下机械外破隐患目标检测精度不高，容易出现错检、漏检的问题，文中提出了一种改进YOLOv7(you only look once version 7)的机械外破隐患目标检测算法。文章在检测头网络中添加Swin Transformer注意力机制提高对多尺度特征的提取能力，然后在主干网络中将部分卷积模块替换为深度可分离卷积，降低模型运算成本，采用Focal-EIOU( Focal and enhanced intersection over union)损失函数优化预测框，最后引入Mish激活函数增强网络的泛化能力，提高模型在复杂背景、目标部分被遮挡情况下的检测性能。实验结果表明，改进后的算法较原YOLOv7在准确率、召回率和平均精度均值上分别提高了5.2%、10.6%和5.2%，较其他主流算法在检测精度和模型体积上有着明显的优势，验证了改进方法的有效性，为复杂场景下机械外破隐患目标的边缘识别提供算法支持。

英文摘要:

Aiming at the problem that the detection accuracy of hidden objects on mechanical external damage is not high, which is prone to appear false detection and missed detection in the case of complex background, large scale change and occlusion, a hidden target detection algorithm for mechanical external damage based on improved YOLOv7(you only look once version 7) is proposed. Firstly, the Swin Transformer attention mechanism is added to the Head to improve the extraction ability of multi-scale features. Then, some convolutions in the Backbone are replaced by depth-wise separable convolution to reduce the operation cost of the model. While, the Focal-EIOU loss function is used to optimize the prediction box. Finally, the Mish activation function is introduced to enhance the generalization ability of the network, and improve the detection performance of the model when the complex background and the object are partially obscured. The experimental results show that the improved algorithm is 5.2% , 10.6% , and 5.2% higher than the original YOLOv7 in accuracy, recall, and mean average precision, respectively. Compared with other mainstream algorithms, it has obvious advantages in detection accuracy and model volume. The effectiveness of the improved method is verified, which provides algorithm support for edge recognition of mechanical external damage hidden in complex backgrounds.

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