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A Robust Faster R-CNN Model with Feature Enhancement for Rust Detection of Transmission Line Fitting

Rust of transmission line fittings is a major hidden risk to transmission safety. Since the fittings located at high altitude are inconvenient to detect and maintain, machine vision techniques have been introduced to realize the intelligent rust detection with the help of unmanned aerial vehicles (U...

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Autores principales: Guo, Zhimin, Tian, Yangyang, Mao, Wandeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611128/
https://www.ncbi.nlm.nih.gov/pubmed/36298312
http://dx.doi.org/10.3390/s22207961
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author Guo, Zhimin
Tian, Yangyang
Mao, Wandeng
author_facet Guo, Zhimin
Tian, Yangyang
Mao, Wandeng
author_sort Guo, Zhimin
collection PubMed
description Rust of transmission line fittings is a major hidden risk to transmission safety. Since the fittings located at high altitude are inconvenient to detect and maintain, machine vision techniques have been introduced to realize the intelligent rust detection with the help of unmanned aerial vehicles (UAV). Due to the small size of fittings and disturbance of complex environmental background, however, there are often cases of missing detection and false detection. To improve the detection reliability and robustness, this paper proposes a new robust Faster R-CNN model with feature enhancement mechanism for the rust detection of transmission line fitting. Different from current methods that improve feature representation in front end, this paper adopts an idea of back-end feature enhancement. First, the residual network ResNet-101 is introduced as the backbone network to extract rich discriminative information from the UAV images. Second, a new feature enhancement mechanism is added after the region of interest (ROI) pooling layer. Through calculating the similarity between each region proposal and the others, the feature weights of the region proposals containing target object can be enhanced via the overlaying of the object’s representation. The weight of the disturbance terms can then be relatively reduced. Empirical evaluation is conducted on some real-world UAV monitoring images. The comparative results demonstrate the effectiveness of the proposed model in terms of detection precision and recall rate, with the average precision of rust detection 97.07%, indicating that the proposed method can provide an reliable and robust solution for the rust detection.
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spelling pubmed-96111282022-10-28 A Robust Faster R-CNN Model with Feature Enhancement for Rust Detection of Transmission Line Fitting Guo, Zhimin Tian, Yangyang Mao, Wandeng Sensors (Basel) Article Rust of transmission line fittings is a major hidden risk to transmission safety. Since the fittings located at high altitude are inconvenient to detect and maintain, machine vision techniques have been introduced to realize the intelligent rust detection with the help of unmanned aerial vehicles (UAV). Due to the small size of fittings and disturbance of complex environmental background, however, there are often cases of missing detection and false detection. To improve the detection reliability and robustness, this paper proposes a new robust Faster R-CNN model with feature enhancement mechanism for the rust detection of transmission line fitting. Different from current methods that improve feature representation in front end, this paper adopts an idea of back-end feature enhancement. First, the residual network ResNet-101 is introduced as the backbone network to extract rich discriminative information from the UAV images. Second, a new feature enhancement mechanism is added after the region of interest (ROI) pooling layer. Through calculating the similarity between each region proposal and the others, the feature weights of the region proposals containing target object can be enhanced via the overlaying of the object’s representation. The weight of the disturbance terms can then be relatively reduced. Empirical evaluation is conducted on some real-world UAV monitoring images. The comparative results demonstrate the effectiveness of the proposed model in terms of detection precision and recall rate, with the average precision of rust detection 97.07%, indicating that the proposed method can provide an reliable and robust solution for the rust detection. MDPI 2022-10-19 /pmc/articles/PMC9611128/ /pubmed/36298312 http://dx.doi.org/10.3390/s22207961 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guo, Zhimin
Tian, Yangyang
Mao, Wandeng
A Robust Faster R-CNN Model with Feature Enhancement for Rust Detection of Transmission Line Fitting
title A Robust Faster R-CNN Model with Feature Enhancement for Rust Detection of Transmission Line Fitting
title_full A Robust Faster R-CNN Model with Feature Enhancement for Rust Detection of Transmission Line Fitting
title_fullStr A Robust Faster R-CNN Model with Feature Enhancement for Rust Detection of Transmission Line Fitting
title_full_unstemmed A Robust Faster R-CNN Model with Feature Enhancement for Rust Detection of Transmission Line Fitting
title_short A Robust Faster R-CNN Model with Feature Enhancement for Rust Detection of Transmission Line Fitting
title_sort robust faster r-cnn model with feature enhancement for rust detection of transmission line fitting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611128/
https://www.ncbi.nlm.nih.gov/pubmed/36298312
http://dx.doi.org/10.3390/s22207961
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