Title: Research on efficient detection method of low voltage power line based on edge intelligence

Authors: Ziwen Cai; Yun Zhao; Yong Xiao; Yuxin Lu; Haolin Wang

Addresses: CSG Electric Power Research Institute, Guangzhou, 510640, China ' CSG Electric Power Research Institute, Guangzhou, 510640, China ' CSG Electric Power Research Institute, Guangzhou, 510640, China ' CSG Electric Power Research Institute, Guangzhou, 510640, China ' CSG Electric Power Research Institute, Guangzhou, 510640, China

Abstract: In view of the complex background of overhead lines in low-voltage distribution networks in aerial photos, power lines are blurred, and the characteristics of the lines will be seriously weakened. A context-based Gabor YOLO algorithm is proposed for efficient detection of low-voltage power lines. Firstly, an improved Gabor operator is proposed to extract Gabor features from greyscale and Gaussian filtered images to obtain the foreground region of the image. Secondly, an improved YOLO neural network model is used to locate and detect power lines and auxiliary targets in the foreground image, and then conduct experimental verification. Experimental results show that this method has the highest accuracy and extraction speed compared to other methods such as YOLOv4, with a mAP value of 93.6%, which meets the actual work needs.

Keywords: overhead lines; low-voltage; distribution network; Gabor YOLO; edge intelligence.

DOI: 10.1504/IJRIS.2024.143158

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.5, pp.383 - 391

Received: 06 Jan 2023
Accepted: 21 Mar 2023
Published online: 05 Dec 2024 *

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