輕量級YOLOv8的絕緣子缺陷檢測算法

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中圖分類號：TP391.41 文獻標(biāo)志碼：A 文章編號：1672-1098（2025）02-0010-09

引文格式：.輕量級YOLOv8的絕緣子缺陷檢測算法[J].安徽理工大學(xué)學(xué)報（自然科學(xué)版），2025，45（2） ：10-18.

Insulator Defect Detection Algorithm for the Lightweight YOLOv8 ZHOU Mengran，CAI Wentao，LAI Wenhao

ScholfElectricalandInformationEnginering，Anui UniversityofScienceandTechnolog，HuainanAnui32O，China）

Abstract：Objective To address isses such as low detection accuracy，prolonged detection time，and frequent missed or false detections in insulator defect detection methods，an improved detection approach based on YOLOv8 was proposed. Methods First，a lightweight bidirectional feature pyramid lightweight network （BiFPN） was introduced to enhance multi ?- scale feature fusion while reducing computational complexity. Second，the detection head was redesigned by replacing the original convolutions with two re-parameterized convolutions，enabling richer and more complex feature extraction，thereby improving training eficiency and model generalization.Finally，the original lossfunction（CIoU）was replaced with MPDIoU to optimize bounding box regression and enhance detectionaccuracy.Results Experimental results demonstrated that compared to theoriginal model，the improved algorithm reduced computational load by 27.2% ，decreased parameters by 41.2% ，increased FPS by 81.8% ，and improved mAP by 3.2% . Conclusion The proposed method successfully enhanced detection accuracy while maintaining model lightweightness，meeting the practical requirements for insulator defect detection.

Key words ： insulator;defect detection;lightweight; detection head ;loss function

在電力系統(tǒng)中，絕緣子作為電氣設(shè)備的重要組成部分，起著將電力線路導(dǎo)線與支架、橫擔(dān)等接地構(gòu)件隔離的重要作用[1]。（剩余9085字）