Title: Research on high-precision insulator classification using an improved hybrid attention mechanism in the context of unmanned aerial vehicles

Authors: Qi Cao; Song Yang; Haolin Li; Xinqiang Wu; Ming Liu; Zhijun Qin; Feng Wang

Addresses: Baishan Power Supply Company, State Grid Jilin Electric Power Co, Ltd., Baishan, 134300, China ' Baishan Power Supply Company, State Grid Jilin Electric Power Co, Ltd., Baishan, 134300, China ' Baishan Power Supply Company, State Grid Jilin Electric Power Co, Ltd., Baishan, 134300, China ' Baishan Power Supply Company, State Grid Jilin Electric Power Co, Ltd., Baishan, 134300, China ' Baishan Power Supply Company, State Grid Jilin Electric Power Co, Ltd., Baishan, 134300, China ' Baishan Power Supply Company, State Grid Jilin Electric Power Co, Ltd., Baishan, 134300, China ' Baishan Power Supply Company, State Grid Jilin Electric Power Co, Ltd., Baishan, 134300, China

Abstract: With the increasing reliance on unmanned aerial vehicles (UAVs) for power transmission line inspections, there has been a growing need for more sophisticated image classification methods to analyse the vast number of images captured during these operations. Traditional methods often struggle with the accurate identification of critical components, such as insulators, which are essential for maintaining the integrity of power lines. In this study, we present an improved vision transformer (ViT) model specifically designed to classify insulator conditions into three categories: damaged, flashover, and normal. To facilitate this, we constructed a dedicated dataset featuring these three conditions of insulators. Our enhanced ViT model demonstrates superior performance compared to conventional deep learning models, offering higher accuracy and robustness in identifying insulator conditions. The experimental results highlight the model's effectiveness, with significant improvements in classification accuracy, making it a valuable tool for enhancing the reliability and safety of power transmission line inspections.

Keywords: power transmission line inspection; insulator condition classification; UAV; unmanned aerial vehicle; deep learning; image classification.

DOI: 10.1504/IJCSM.2025.151192

International Journal of Computing Science and Mathematics, 2025 Vol.22 No.3, pp.247 - 265

Received: 17 Apr 2025
Accepted: 03 Aug 2025
Published online: 16 Jan 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article