@article{Yan2025, 
author = {Xunguang Yan and Wenrui Wang and Fanglin Lu and Hongyong Fan and Bo Wu and Jianfeng Yu},
title = {GFRF R-CNN: Object Detection Algorithm for Transmission Lines},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {82},
number = {1},
pages = {1439-1458},
keywords = {Faster R-CNN, transmission line, object detection, GIOU, GFR},
url = {https://www.sciopen.com/article/10.32604/cmc.2024.057797},
doi = {10.32604/cmc.2024.057797},
abstract = {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 Region-based 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 the model’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.}
}