An Enhanced YOLOv5s Model With UAV Flight Data Fusion for Defect Detection in Power Transmission Lines

What the paper says

Traditional transmission line inspection methods face limitations including low efficiency, high costs, and significant safety risks. Although unmanned aerial vehicle inspection has become a mainstream trend, the efficient processing of the massive amounts of image data it generates, as well as the accurate identification and spatial localization of multi‐type and multi‐scale defects against complex backgrounds, represents key technical challenges currently faced by the intelligent transmission operation and maintenance. To address these challenges, the study proposes and constructs an end‐to‐end intelligent safety supervision system for unmanned aerial vehicles. Firstly, this system achieves standardised and automated collection of inspection data through autonomous flight route planning. Secondly, to ensure data security, a localization algorithm that integrates unmanned aerial vehicle flight control data is designed. Experimental results demonstrate that our proposed method achieves outstanding performance. Initially, the accurate retrieval model constructed for massive amounts of unmanned aerial vehicle inspection data in the power grid achieves a retrieval accuracy exceeding 75%. Building on this, the core high‐precision defect detection model performs outstandingly, with an average detection rate reaching 83%. Specifically, the detection rate for channel defects is 85%, for unclear text and images on signs (ancillary facilities) is 80%, for damaged lightning rods (ancillary facilities) and damaged protective caps (foundations) is 79%, and for damaged armour rods (hardware) is 72%, verifying the model's effectiveness in identifying multiple types of defects. The research work establishes a complete technological chain from unmanned aerial vehicle data processing and intelligent defect detection to precise spatial localization. The proposed method meets practical application requirements in terms of both identification accuracy and category breadth.