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Insulators' Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models
Insulators identification and their missing defect detection are of paramount importance for the intelligent inspection of high-voltage transmission lines. As the backgrounds are complex, some insulators may be occluded, and the missing defect of the insulator is so small that it is not easily detec...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9402330/ https://www.ncbi.nlm.nih.gov/pubmed/36035858 http://dx.doi.org/10.1155/2022/7113765 |
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author | Liu, Jingjing Liu, Chuanyang Wu, Yiquan Sun, Zuo Xu, Huajie |
author_facet | Liu, Jingjing Liu, Chuanyang Wu, Yiquan Sun, Zuo Xu, Huajie |
author_sort | Liu, Jingjing |
collection | PubMed |
description | Insulators identification and their missing defect detection are of paramount importance for the intelligent inspection of high-voltage transmission lines. As the backgrounds are complex, some insulators may be occluded, and the missing defect of the insulator is so small that it is not easily detected from aerial images with different backgrounds. To address the above issues, in this study, a cascaded You Only Look Once (YOLO) models are mainly explored to perform insulators and their defect detection in aerial images. Firstly, the datasets used for insulators location and missing defect detection are created. Secondly, a new model is proposed to locate the position of insulators, which is improved in the feature extraction network and multisacle prediction network based on previous YOLOv3-dense model. An improved YOLOv4-tiny model is used to conduct missing defect detection on the detected insulators. And then, the proposed YOLO models are trained and tested on the built datasets, respectively. Finally, the final models are cascaded for insulators identification and their missing defect detection. The average precision of missing defect detection can reach 98.4%, which is 5.2% higher than that of faster RCNN and 10.2% higher than that of SSD. The running time of the cascaded YOLO models for missing defect detection can reach 106 frames per second. Extensive experiments demonstrate that the proposed deep learning models achieve good performance in insulator identification and its missing defect detection from the inspection of high-voltage transmission lines. |
format | Online Article Text |
id | pubmed-9402330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94023302022-08-25 Insulators' Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models Liu, Jingjing Liu, Chuanyang Wu, Yiquan Sun, Zuo Xu, Huajie Comput Intell Neurosci Research Article Insulators identification and their missing defect detection are of paramount importance for the intelligent inspection of high-voltage transmission lines. As the backgrounds are complex, some insulators may be occluded, and the missing defect of the insulator is so small that it is not easily detected from aerial images with different backgrounds. To address the above issues, in this study, a cascaded You Only Look Once (YOLO) models are mainly explored to perform insulators and their defect detection in aerial images. Firstly, the datasets used for insulators location and missing defect detection are created. Secondly, a new model is proposed to locate the position of insulators, which is improved in the feature extraction network and multisacle prediction network based on previous YOLOv3-dense model. An improved YOLOv4-tiny model is used to conduct missing defect detection on the detected insulators. And then, the proposed YOLO models are trained and tested on the built datasets, respectively. Finally, the final models are cascaded for insulators identification and their missing defect detection. The average precision of missing defect detection can reach 98.4%, which is 5.2% higher than that of faster RCNN and 10.2% higher than that of SSD. The running time of the cascaded YOLO models for missing defect detection can reach 106 frames per second. Extensive experiments demonstrate that the proposed deep learning models achieve good performance in insulator identification and its missing defect detection from the inspection of high-voltage transmission lines. Hindawi 2022-08-17 /pmc/articles/PMC9402330/ /pubmed/36035858 http://dx.doi.org/10.1155/2022/7113765 Text en Copyright © 2022 Jingjing Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Jingjing Liu, Chuanyang Wu, Yiquan Sun, Zuo Xu, Huajie Insulators' Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models |
title | Insulators' Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models |
title_full | Insulators' Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models |
title_fullStr | Insulators' Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models |
title_full_unstemmed | Insulators' Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models |
title_short | Insulators' Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models |
title_sort | insulators' identification and missing defect detection in aerial images based on cascaded yolo models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9402330/ https://www.ncbi.nlm.nih.gov/pubmed/36035858 http://dx.doi.org/10.1155/2022/7113765 |
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