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A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and h...

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Autores principales: Zhang, Tong, Zhang, Yinan, Xin, Min, Liao, Jiashe, Xie, Qingfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256046/
https://www.ncbi.nlm.nih.gov/pubmed/37299976
http://dx.doi.org/10.3390/s23115249
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author Zhang, Tong
Zhang, Yinan
Xin, Min
Liao, Jiashe
Xie, Qingfeng
author_facet Zhang, Tong
Zhang, Yinan
Xin, Min
Liao, Jiashe
Xie, Qingfeng
author_sort Zhang, Tong
collection PubMed
description Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced the Ghost module into the YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance of unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention modules (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) is set to 0.5, and the mAP is set from 0.5 to 0.95 of our model and can reach 99.4% and 91.7%; the parameters and model size were reduced to 3,807,372 and 8.79 M, which can be easily deployed to embedded devices such as UAVs. Moreover, the speed of detection can reach 10.9 ms/image, which can meet the real-time detection requirement.
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spelling pubmed-102560462023-06-10 A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5 Zhang, Tong Zhang, Yinan Xin, Min Liao, Jiashe Xie, Qingfeng Sensors (Basel) Article Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced the Ghost module into the YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance of unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention modules (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) is set to 0.5, and the mAP is set from 0.5 to 0.95 of our model and can reach 99.4% and 91.7%; the parameters and model size were reduced to 3,807,372 and 8.79 M, which can be easily deployed to embedded devices such as UAVs. Moreover, the speed of detection can reach 10.9 ms/image, which can meet the real-time detection requirement. MDPI 2023-06-01 /pmc/articles/PMC10256046/ /pubmed/37299976 http://dx.doi.org/10.3390/s23115249 Text en © 2023 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
Zhang, Tong
Zhang, Yinan
Xin, Min
Liao, Jiashe
Xie, Qingfeng
A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5
title A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5
title_full A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5
title_fullStr A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5
title_full_unstemmed A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5
title_short A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5
title_sort light-weight network for small insulator and defect detection using uav imaging based on improved yolov5
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256046/
https://www.ncbi.nlm.nih.gov/pubmed/37299976
http://dx.doi.org/10.3390/s23115249
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