A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection

Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight d...
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Autor*in:	Cong Liu [verfasserIn] Wentao Yi [verfasserIn] Min Liu [verfasserIn] Yifeng Wang [verfasserIn] Sheng Hu [verfasserIn] Minghu Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	image recognition YOLOv5s detection of insulator defects lightweight ghost

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 12(2023), 4292, p 4292
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:4292, p 4292

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics12204292

Katalog-ID:	DOAJ093149131

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allfields	10.3390/electronics12204292 doi (DE-627)DOAJ093149131 (DE-599)DOAJf1dac8167b944a07bc20c32540587398 DE-627 ger DE-627 rakwb eng TK7800-8360 Cong Liu verfasserin aut A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices. image recognition YOLOv5s detection of insulator defects lightweight ghost Electronics Wentao Yi verfasserin aut Min Liu verfasserin aut Yifeng Wang verfasserin aut Sheng Hu verfasserin aut Minghu Wu verfasserin aut In Electronics MDPI AG, 2013 12(2023), 4292, p 4292 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:4292, p 4292 https://doi.org/10.3390/electronics12204292 kostenfrei https://doaj.org/article/f1dac8167b944a07bc20c32540587398 kostenfrei https://www.mdpi.com/2079-9292/12/20/4292 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 4292, p 4292
spelling	10.3390/electronics12204292 doi (DE-627)DOAJ093149131 (DE-599)DOAJf1dac8167b944a07bc20c32540587398 DE-627 ger DE-627 rakwb eng TK7800-8360 Cong Liu verfasserin aut A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices. image recognition YOLOv5s detection of insulator defects lightweight ghost Electronics Wentao Yi verfasserin aut Min Liu verfasserin aut Yifeng Wang verfasserin aut Sheng Hu verfasserin aut Minghu Wu verfasserin aut In Electronics MDPI AG, 2013 12(2023), 4292, p 4292 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:4292, p 4292 https://doi.org/10.3390/electronics12204292 kostenfrei https://doaj.org/article/f1dac8167b944a07bc20c32540587398 kostenfrei https://www.mdpi.com/2079-9292/12/20/4292 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 4292, p 4292
allfields_unstemmed	10.3390/electronics12204292 doi (DE-627)DOAJ093149131 (DE-599)DOAJf1dac8167b944a07bc20c32540587398 DE-627 ger DE-627 rakwb eng TK7800-8360 Cong Liu verfasserin aut A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices. image recognition YOLOv5s detection of insulator defects lightweight ghost Electronics Wentao Yi verfasserin aut Min Liu verfasserin aut Yifeng Wang verfasserin aut Sheng Hu verfasserin aut Minghu Wu verfasserin aut In Electronics MDPI AG, 2013 12(2023), 4292, p 4292 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:4292, p 4292 https://doi.org/10.3390/electronics12204292 kostenfrei https://doaj.org/article/f1dac8167b944a07bc20c32540587398 kostenfrei https://www.mdpi.com/2079-9292/12/20/4292 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 4292, p 4292
allfieldsGer	10.3390/electronics12204292 doi (DE-627)DOAJ093149131 (DE-599)DOAJf1dac8167b944a07bc20c32540587398 DE-627 ger DE-627 rakwb eng TK7800-8360 Cong Liu verfasserin aut A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices. image recognition YOLOv5s detection of insulator defects lightweight ghost Electronics Wentao Yi verfasserin aut Min Liu verfasserin aut Yifeng Wang verfasserin aut Sheng Hu verfasserin aut Minghu Wu verfasserin aut In Electronics MDPI AG, 2013 12(2023), 4292, p 4292 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:4292, p 4292 https://doi.org/10.3390/electronics12204292 kostenfrei https://doaj.org/article/f1dac8167b944a07bc20c32540587398 kostenfrei https://www.mdpi.com/2079-9292/12/20/4292 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 4292, p 4292
allfieldsSound	10.3390/electronics12204292 doi (DE-627)DOAJ093149131 (DE-599)DOAJf1dac8167b944a07bc20c32540587398 DE-627 ger DE-627 rakwb eng TK7800-8360 Cong Liu verfasserin aut A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices. image recognition YOLOv5s detection of insulator defects lightweight ghost Electronics Wentao Yi verfasserin aut Min Liu verfasserin aut Yifeng Wang verfasserin aut Sheng Hu verfasserin aut Minghu Wu verfasserin aut In Electronics MDPI AG, 2013 12(2023), 4292, p 4292 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:4292, p 4292 https://doi.org/10.3390/electronics12204292 kostenfrei https://doaj.org/article/f1dac8167b944a07bc20c32540587398 kostenfrei https://www.mdpi.com/2079-9292/12/20/4292 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 4292, p 4292
language	English
source	In Electronics 12(2023), 4292, p 4292 volume:12 year:2023 number:4292, p 4292
sourceStr	In Electronics 12(2023), 4292, p 4292 volume:12 year:2023 number:4292, p 4292
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	image recognition YOLOv5s detection of insulator defects lightweight ghost Electronics
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container_title	Electronics
authorswithroles_txt_mv	Cong Liu @@aut@@ Wentao Yi @@aut@@ Min Liu @@aut@@ Yifeng Wang @@aut@@ Sheng Hu @@aut@@ Minghu Wu @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. 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callnumber-first	T - Technology
author	Cong Liu
spellingShingle	Cong Liu misc TK7800-8360 misc image recognition misc YOLOv5s misc detection of insulator defects misc lightweight misc ghost misc Electronics A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection
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topic_title	TK7800-8360 A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection image recognition YOLOv5s detection of insulator defects lightweight ghost
topic	misc TK7800-8360 misc image recognition misc YOLOv5s misc detection of insulator defects misc lightweight misc ghost misc Electronics
topic_unstemmed	misc TK7800-8360 misc image recognition misc YOLOv5s misc detection of insulator defects misc lightweight misc ghost misc Electronics
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title	A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection
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title_full	A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection
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title_sort	lightweight network based on improved yolov5s for insulator defect detection
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title_auth	A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection
abstract	Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices.
abstractGer	Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices.
abstract_unstemmed	Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices.
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title_short	A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection
url	https://doi.org/10.3390/electronics12204292 https://doaj.org/article/f1dac8167b944a07bc20c32540587398 https://www.mdpi.com/2079-9292/12/20/4292 https://doaj.org/toc/2079-9292
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