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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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

Received: 26 February 2025

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	SONG Xinjie
	JIN Yiming

Cite this article:

SONG Xinjie,JIN Yiming. Research on deep learning based insulator recognition method for overhead transmission lines[J]. Electrical Engineering, 2025, 26(9): 62-68.

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

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