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Insulator-Defect Detection Algorithm Based on Improved YOLOv7

Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target b...

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Autores principales: Zheng, Jianfeng, Wu, Hang, Zhang, Han, Wang, Zhaoqi, Xu, Weiyue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697038/
https://www.ncbi.nlm.nih.gov/pubmed/36433397
http://dx.doi.org/10.3390/s22228801
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author Zheng, Jianfeng
Wu, Hang
Zhang, Han
Wang, Zhaoqi
Xu, Weiyue
author_facet Zheng, Jianfeng
Wu, Hang
Zhang, Han
Wang, Zhaoqi
Xu, Weiyue
author_sort Zheng, Jianfeng
collection PubMed
description Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target boxes of the insulator dataset are clustered based on K-means++ to generate more suitable anchor boxes for detecting insulator-defect targets. Secondly, the Coordinate Attention (CoordAtt) module and HorBlock module are added to the network. Then, in the channel and spatial domains, the network can enhance the effective features of the feature-extraction process and weaken the ineffective features. Finally, the SCYLLA-IoU (SIoU) and focal loss functions are used to accelerate the convergence of the model and solve the imbalance of positive and negative samples. Furthermore, to optimize the overall performance of the model, the method of non-maximum suppression (NMS) is improved to reduce accidental deletion and false detection of defect targets. The experimental results show that the mean average precision of our model is 93.8%, higher than the Faster R-CNN model, the YOLOv7 model, and YOLOv5s model by 7.6%, 3.7%, and 4%, respectively. The proposed YOLOv7 model can effectively realize the accurate detection of small objects in complex backgrounds.
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spelling pubmed-96970382022-11-26 Insulator-Defect Detection Algorithm Based on Improved YOLOv7 Zheng, Jianfeng Wu, Hang Zhang, Han Wang, Zhaoqi Xu, Weiyue Sensors (Basel) Article Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target boxes of the insulator dataset are clustered based on K-means++ to generate more suitable anchor boxes for detecting insulator-defect targets. Secondly, the Coordinate Attention (CoordAtt) module and HorBlock module are added to the network. Then, in the channel and spatial domains, the network can enhance the effective features of the feature-extraction process and weaken the ineffective features. Finally, the SCYLLA-IoU (SIoU) and focal loss functions are used to accelerate the convergence of the model and solve the imbalance of positive and negative samples. Furthermore, to optimize the overall performance of the model, the method of non-maximum suppression (NMS) is improved to reduce accidental deletion and false detection of defect targets. The experimental results show that the mean average precision of our model is 93.8%, higher than the Faster R-CNN model, the YOLOv7 model, and YOLOv5s model by 7.6%, 3.7%, and 4%, respectively. The proposed YOLOv7 model can effectively realize the accurate detection of small objects in complex backgrounds. MDPI 2022-11-14 /pmc/articles/PMC9697038/ /pubmed/36433397 http://dx.doi.org/10.3390/s22228801 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
Zheng, Jianfeng
Wu, Hang
Zhang, Han
Wang, Zhaoqi
Xu, Weiyue
Insulator-Defect Detection Algorithm Based on Improved YOLOv7
title Insulator-Defect Detection Algorithm Based on Improved YOLOv7
title_full Insulator-Defect Detection Algorithm Based on Improved YOLOv7
title_fullStr Insulator-Defect Detection Algorithm Based on Improved YOLOv7
title_full_unstemmed Insulator-Defect Detection Algorithm Based on Improved YOLOv7
title_short Insulator-Defect Detection Algorithm Based on Improved YOLOv7
title_sort insulator-defect detection algorithm based on improved yolov7
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697038/
https://www.ncbi.nlm.nih.gov/pubmed/36433397
http://dx.doi.org/10.3390/s22228801
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