Dual-backbone Network Insulator Defect Detection Algorithm Based on YOLOv5

doi:10.3969/j.issn.1006-2475.2025.08.012

Abstract

Abstract:
Abstract： The current insulator defect detection algorithm has problems such as insufficient feature extraction， poor small target detection effect， and unbalanced overall algorithm performance. To address the above problems， this paper proposes a dual-trunk network insulator defect detection algorithm based on YOLOv5. Firstly， this paper designs a new dual-trunk feature extraction network GELAN-Ghost suitable for YOLOv5. This module can more fully extract feature information while maintaining lightweight. Secondly， using the idea of inverted residual structure， a plug-and-play efficient multi-scale attention module iEMA is designed in the neck network part of the algorithm. Finally， a new dynamic detection head DynamicHeadv3 is designed to replace the original detection head to extract richer features and enhance the perception ability of the model. The experimental results show that the improved model has an accuracy improvement of 1.4 percentage points， a parameter amount and a computational amount reduced by 46% and 33% respectively， and the detection speed has also been improved to a certain extent. The performance has achieved a good balance， which is more in line with the needs of drone and edge insulator defect detection.

Key words: Key words： insulator defect detection, YOLOv5, dual-backbone network, iEMA attention mechanism, dynamic detection head

CLC Number:

中图分类号：TP391

GAO Lisha1, PENG Guozheng2, WANG Chong1, HAN Shuo1, XIANG Nan1, ZHANG Qizhe2, ZHU Yuefei3, CHENG Xu3. Dual-backbone Network Insulator Defect Detection Algorithm Based on YOLOv5[J]. Computer and Modernization, 2025, 0(08): 82-88.

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