基于深度学习的架空输电线路绝缘子识别方法研究

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摘要绝缘子存在破损、裂纹和污秽等问题会影响电力系统运行稳定性和供电可靠性,为此本文提出一种基于深度学习的架空输电线路绝缘子识别方法,以提升架空输电线路绝缘子识别精度,辅助绝缘子巡检工作。在YOLOv7-tiny模型中引入坐标注意力（CA）机制,并将卷积核为3×3、步幅为1的卷积替换为可变形卷积（DCN）v2;将损失函数交并比（IoU）值替换为归一化Wasserstein距离（NWD）,提升复杂环境和遮挡环境下的识别能力。实验结果表明,与更快区域卷积神经网络（Faster R-CNN）、YOLOv5s、YOLOv7和原始YOLOv7-tiny模型相比,改进YOLOv7-tiny模型的平均检测精度分别提升了8.6、4.7、0.8和3.4个百分点,可有效实现对绝缘子的准确识别。

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	宋欣杰
	金一鸣

关键词 ：深度学习, 架空输电线路, 绝缘子识别

Abstract：Insulators with defects such as breakage, cracks and drops affect the stability of power system operation and reliability of power supply. Thus this paper proposes a deep learning based insulator recognition method for overhead transmission lines to improve the recognition accuracy. The coordinate attention (CA) mechanism is introduced into you only look once (YOLO) v7-tiny model and the convolution kernel of 3×3 with a step size of 1 is replaced by the deformable convolutional network (DCN) v2. The intersection over union (IoU) value of loss function is replaced by the normalized Wasserstein distance (NWD) to improve the insulator recognition capability in complex and occluded environments. The experimental results show that the improved YOLOv7-tiny model can effectively enhance the insulator recognition accuracy by improving the mean average precision (mAP) by 8.6%, 4.7%, 0.8%, and 3.4% compared with faster region convolutional neural network (Faster R-CNN), YOLOv5s, YOLOv7, and the original YOLOv7-tiny, respectively.

Key words： deep learning overhead transmission lines insulator recognition

收稿日期: 2025-02-26

作者简介: 宋欣杰（1998—）,男,山东省烟台市莱阳市人,硕士,助理工程师,主要从事深度学习、变电运检与继电保护方面的研究工作。

引用本文:

宋欣杰, 金一鸣. 基于深度学习的架空输电线路绝缘子识别方法研究[J]. 电气技术, 2025, 26(9): 62-68. SONG Xinjie, JIN Yiming. Research on deep learning based insulator recognition method for overhead transmission lines. Electrical Engineering, 2025, 26(9): 62-68.

链接本文:

https://dqjs.cesmedia.cn/CN/Y2025/V26/I9/62

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