【摘要】 电网绝缘子的识别及定位是电网运行状态有效检测的前提，基于无人机拍摄的输电线路复合绝缘子图像，为解决传统人工巡检输电线路安全系数高、作业效率低下的问题，提出了基于改进的卷积神经网络绝缘子设备研究识别的方法。算法通过添加注意力机制CBAM和SENet，实现对输电线路器件定位的有效改进，实验结果表示，改进的模型较之前减少了运行时间，目标检测精度提高了8.2%，有效提高了对输电线路绝缘子检测的性能和鲁棒性，取得了比已有算法更有优势的检测结果。更多还原

【Abstract】 The identification and positioning of power grid insulators is the premise for effective detection of power grid opera-tion status. Based on the composite insulator images of transmission lines captured by drones,in order to solve the problems of highsafety factor and low operation efficiency of traditional manual inspection of transmission lines,an improved method is proposed,which is convolutional neural network insulator device research recognition algorithm. By adding attention mechanism CBAM andSENet,the algorithm can effectively improve the positioning of transmission line devices. Experimental results show that the im-proved model reduces execution time compared to previous versions,and the target detection accuracy increases by 8.2%. Thismethod significantly enhances the performance and robustness of the system for detecting insulators on transmission lines,providingmore effective results compared to existing algorithms.更多还原