Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2024.057797
Journal Menu
Special Issues
Table of Content

All issues

Online First

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

GFRF R-CNN: Object Detection Algorithm for Transmission Lines

Xunguang Yan1,2, Wenrui Wang1, Fanglin Lu1, Hongyong Fan3, Bo Wu1, Jianfeng Yu1,*
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
* Corresponding Author: Jianfeng Yu. Email: email
(This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057797

Received 27 August 2024; Accepted 30 October 2024; Published online 26 November 2024

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.

Keywords

Faster R-CNN; transmission line; object detection; GIOU; GFR

  • 152

    View

  • 27

    Download

  • 0

    Like

Related articles
Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link