Insulator Defect Detection by UAV Based on Lightweight YOLOv8

doi:10.3969/j.issn.1006-2475.2025.07.002

Abstract

Abstract:
Abstract: In order to solve the problems of insulator string and insulator self-explosion， damage， flashover defects in complex background， different scales， small target factors leading to false detection and missed detection， resulting in low detection accuracy， the CPCW-YOLOv8 algorithm is proposed. Firstly， a lightweight CBAM attention mechanism is introduced into the backbone part， so that the model can enhance the extraction ability of insulator strings and insulator defect features in complex backgrounds from both channel and space aspects. Then， the small target detection layer is added， and the multi-scale fusion is used to enhance the extraction of shallow semantic information by the network， so as to capture more details of insulator defects and improve the detection accuracy of small targets. Secondly， in order to make the model more lightweight， a lightweight module C2f-Faster is constructed. Finally， the original CIoU is optimized to WIoU to accelerate convergence and improve the detection accuracy. Experimental results show that compared with the original model， the number of parameters of CPCW-YOLOv8 is reduced by 12.6 precentage points， and the average accuracy is increased by 5.2 precentage points. The proposed network provides a more efficient method for the defect detection of insulators in power systems.

Key words: Key words: insulator detection, small target detection, lightweight modules, loss function

CLC Number:

TP391
TP399

JIANG Zhiwei1, FU Xiaojin1, CHEN Wenbin1, JIANG Yichen2. Insulator Defect Detection by UAV Based on Lightweight YOLOv8 [J]. Computer and Modernization, 2025, 0(07): 9-14.

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