Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms

The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain;...
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Autor*in:	Teng Li [verfasserIn] Tian Hao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	catenary insulator damage detection deep learning Mask R-CNN Zernike moment

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 12(2022), 10, p 5004
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:10, p 5004

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app12105004

Katalog-ID:	DOAJ022352368

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520			\|a The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios.
650		4	\|a catenary insulator
650		4	\|a damage detection
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650		4	\|a Mask R-CNN
650		4	\|a Zernike moment
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allfields	10.3390/app12105004 doi (DE-627)DOAJ022352368 (DE-599)DOAJ4509747c31cd4dcf8f1cc803c4d9be42 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Teng Li verfasserin aut Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios. catenary insulator damage detection deep learning Mask R-CNN Zernike moment Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Tian Hao verfasserin aut In Applied Sciences MDPI AG, 2012 12(2022), 10, p 5004 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:12 year:2022 number:10, p 5004 https://doi.org/10.3390/app12105004 kostenfrei https://doaj.org/article/4509747c31cd4dcf8f1cc803c4d9be42 kostenfrei https://www.mdpi.com/2076-3417/12/10/5004 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 2022 10, p 5004
spelling	10.3390/app12105004 doi (DE-627)DOAJ022352368 (DE-599)DOAJ4509747c31cd4dcf8f1cc803c4d9be42 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Teng Li verfasserin aut Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios. catenary insulator damage detection deep learning Mask R-CNN Zernike moment Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Tian Hao verfasserin aut In Applied Sciences MDPI AG, 2012 12(2022), 10, p 5004 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:12 year:2022 number:10, p 5004 https://doi.org/10.3390/app12105004 kostenfrei https://doaj.org/article/4509747c31cd4dcf8f1cc803c4d9be42 kostenfrei https://www.mdpi.com/2076-3417/12/10/5004 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 2022 10, p 5004
allfields_unstemmed	10.3390/app12105004 doi (DE-627)DOAJ022352368 (DE-599)DOAJ4509747c31cd4dcf8f1cc803c4d9be42 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Teng Li verfasserin aut Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios. catenary insulator damage detection deep learning Mask R-CNN Zernike moment Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Tian Hao verfasserin aut In Applied Sciences MDPI AG, 2012 12(2022), 10, p 5004 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:12 year:2022 number:10, p 5004 https://doi.org/10.3390/app12105004 kostenfrei https://doaj.org/article/4509747c31cd4dcf8f1cc803c4d9be42 kostenfrei https://www.mdpi.com/2076-3417/12/10/5004 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 2022 10, p 5004
allfieldsGer	10.3390/app12105004 doi (DE-627)DOAJ022352368 (DE-599)DOAJ4509747c31cd4dcf8f1cc803c4d9be42 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Teng Li verfasserin aut Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios. catenary insulator damage detection deep learning Mask R-CNN Zernike moment Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Tian Hao verfasserin aut In Applied Sciences MDPI AG, 2012 12(2022), 10, p 5004 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:12 year:2022 number:10, p 5004 https://doi.org/10.3390/app12105004 kostenfrei https://doaj.org/article/4509747c31cd4dcf8f1cc803c4d9be42 kostenfrei https://www.mdpi.com/2076-3417/12/10/5004 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 2022 10, p 5004
allfieldsSound	10.3390/app12105004 doi (DE-627)DOAJ022352368 (DE-599)DOAJ4509747c31cd4dcf8f1cc803c4d9be42 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Teng Li verfasserin aut Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios. catenary insulator damage detection deep learning Mask R-CNN Zernike moment Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Tian Hao verfasserin aut In Applied Sciences MDPI AG, 2012 12(2022), 10, p 5004 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:12 year:2022 number:10, p 5004 https://doi.org/10.3390/app12105004 kostenfrei https://doaj.org/article/4509747c31cd4dcf8f1cc803c4d9be42 kostenfrei https://www.mdpi.com/2076-3417/12/10/5004 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 2022 10, p 5004
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callnumber-first	T - Technology
author	Teng Li
spellingShingle	Teng Li misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc catenary insulator misc damage detection misc deep learning misc Mask R-CNN misc Zernike moment misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms catenary insulator damage detection deep learning Mask R-CNN Zernike moment
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc catenary insulator misc damage detection misc deep learning misc Mask R-CNN misc Zernike moment misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_unstemmed	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc catenary insulator misc damage detection misc deep learning misc Mask R-CNN misc Zernike moment misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_browse	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc catenary insulator misc damage detection misc deep learning misc Mask R-CNN misc Zernike moment misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
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title_full	Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
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title_sort	damage detection of insulators in catenary based on deep learning and zernike moment algorithms
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title_auth	Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
abstract	The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios.
abstractGer	The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios.
abstract_unstemmed	The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios.
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title_short	Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
url	https://doi.org/10.3390/app12105004 https://doaj.org/article/4509747c31cd4dcf8f1cc803c4d9be42 https://www.mdpi.com/2076-3417/12/10/5004 https://doaj.org/toc/2076-3417
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However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. 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Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms

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