针对目前基于深度学习的输电线路目标检测存在的小目标特征提取能力较差、易出现误检漏检、检测精度较低、检测速度较慢等问题，提出了一种基于改进深度学习神经网络模型YOLOv7的目标检测方法。首先使用MobileNetV2网络作为YOLOv7的特征提取部分，实现模型的轻量化处理；其次引入注意力（coordinate attention，CA）机制和空洞金字塔池化（atrous spatial pyramid pooling，ASPP）模块来提高模型的精度和感知能力；最后利用建立的输电线路障碍物数据集来训练改进的YOLOv7网络模型，并与原有YOLOv7网络模型进行对比。结果表明，算法在准确率、召回率上显著提升，可满足复杂场景下的输电线路故障检测，更利于模型的嵌入式系统硬件实现。

Aiming at the current target detection methods based on deep learning for transmission line，the feature extraction ability is poor for small target，easy to misdetection leakage detection，detection accuracy is low， detection speed is slow.A transmission line target detection method was proposed based on an improved neural network model YOLOv7.Firstly，the MobileNetV2 network was used as the feature extraction part of YOLOv7 to achieve lightweight processing of the model.Secondly，the CA mechanism and ASPP module were introduced to improve the accuracy and perception of the model.Finally，the self-drawn transmission line obstacle data set was used for training improved YOLOv7 network and compared with the original YOLOv7 model.The results show that the algorithm proposed has significantly improved the accuracy and recall rate，which meets the fault detection in complex scenarios and is more conducive to model deployment of mobile devices and embedded systems.