GFRF R-CNN:Object Detection Algorithm for Transmission Lines  

作　　者：Xunguang Yan Wenrui Wang Fanglin Lu Hongyong Fan Bo Wu Jianfeng Yu 

机构地区：[1]Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai,201210,China [2]University of Chinese Academy of Sciences,Beijing,100049,China [3]Jingwei Textile Machinery Co.,Ltd.,Beijing,100176,China

出　　处：《Computers, Materials & Continua》2025年第1期1439-1458,共20页计算机、材料和连续体（英文）

基　　金：supported by the Shanghai Science and Technology Innovation Action Plan High-Tech Field Project(Grant No.22511100601)for the year 2022 and Technology Development Fund for People’s Livelihood Research(Research on Transmission Line Deep Foundation Pit Environmental Situation Awareness System Based on Multi-Source Data).

摘　　要：To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.

关 键 词：Faster R-CNN transmission line object detection GIOU GFR 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]