Transmission Tower Re-Identification Algorithm Based on Machine Vision

Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lei Chen [verfasserIn] Zuowei Yang [verfasserIn] Fengyun Huang [verfasserIn] Yiwei Dai [verfasserIn] Rui Liu [verfasserIn] Jiajia Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	transmission tower re-identification transmission tower detection YOLO triplet loss

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 14(2024), 2, p 539
Übergeordnetes Werk:	volume:14 ; year:2024 ; number:2, p 539

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app14020539

Katalog-ID:	DOAJ096202130

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allfields	10.3390/app14020539 doi (DE-627)DOAJ096202130 (DE-599)DOAJbc7a602b126a452bba770f63f8a6f126 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Lei Chen verfasserin aut Transmission Tower Re-Identification Algorithm Based on Machine Vision 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%. transmission tower re-identification transmission tower detection YOLO triplet loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zuowei Yang verfasserin aut Fengyun Huang verfasserin aut Yiwei Dai verfasserin aut Rui Liu verfasserin aut Jiajia Li verfasserin aut In Applied Sciences MDPI AG, 2012 14(2024), 2, p 539 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:14 year:2024 number:2, p 539 https://doi.org/10.3390/app14020539 kostenfrei https://doaj.org/article/bc7a602b126a452bba770f63f8a6f126 kostenfrei https://www.mdpi.com/2076-3417/14/2/539 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 14 2024 2, p 539
spelling	10.3390/app14020539 doi (DE-627)DOAJ096202130 (DE-599)DOAJbc7a602b126a452bba770f63f8a6f126 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Lei Chen verfasserin aut Transmission Tower Re-Identification Algorithm Based on Machine Vision 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%. transmission tower re-identification transmission tower detection YOLO triplet loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zuowei Yang verfasserin aut Fengyun Huang verfasserin aut Yiwei Dai verfasserin aut Rui Liu verfasserin aut Jiajia Li verfasserin aut In Applied Sciences MDPI AG, 2012 14(2024), 2, p 539 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:14 year:2024 number:2, p 539 https://doi.org/10.3390/app14020539 kostenfrei https://doaj.org/article/bc7a602b126a452bba770f63f8a6f126 kostenfrei https://www.mdpi.com/2076-3417/14/2/539 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 14 2024 2, p 539
allfields_unstemmed	10.3390/app14020539 doi (DE-627)DOAJ096202130 (DE-599)DOAJbc7a602b126a452bba770f63f8a6f126 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Lei Chen verfasserin aut Transmission Tower Re-Identification Algorithm Based on Machine Vision 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%. transmission tower re-identification transmission tower detection YOLO triplet loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zuowei Yang verfasserin aut Fengyun Huang verfasserin aut Yiwei Dai verfasserin aut Rui Liu verfasserin aut Jiajia Li verfasserin aut In Applied Sciences MDPI AG, 2012 14(2024), 2, p 539 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:14 year:2024 number:2, p 539 https://doi.org/10.3390/app14020539 kostenfrei https://doaj.org/article/bc7a602b126a452bba770f63f8a6f126 kostenfrei https://www.mdpi.com/2076-3417/14/2/539 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 14 2024 2, p 539
allfieldsGer	10.3390/app14020539 doi (DE-627)DOAJ096202130 (DE-599)DOAJbc7a602b126a452bba770f63f8a6f126 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Lei Chen verfasserin aut Transmission Tower Re-Identification Algorithm Based on Machine Vision 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%. transmission tower re-identification transmission tower detection YOLO triplet loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zuowei Yang verfasserin aut Fengyun Huang verfasserin aut Yiwei Dai verfasserin aut Rui Liu verfasserin aut Jiajia Li verfasserin aut In Applied Sciences MDPI AG, 2012 14(2024), 2, p 539 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:14 year:2024 number:2, p 539 https://doi.org/10.3390/app14020539 kostenfrei https://doaj.org/article/bc7a602b126a452bba770f63f8a6f126 kostenfrei https://www.mdpi.com/2076-3417/14/2/539 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 14 2024 2, p 539
allfieldsSound	10.3390/app14020539 doi (DE-627)DOAJ096202130 (DE-599)DOAJbc7a602b126a452bba770f63f8a6f126 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Lei Chen verfasserin aut Transmission Tower Re-Identification Algorithm Based on Machine Vision 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%. transmission tower re-identification transmission tower detection YOLO triplet loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zuowei Yang verfasserin aut Fengyun Huang verfasserin aut Yiwei Dai verfasserin aut Rui Liu verfasserin aut Jiajia Li verfasserin aut In Applied Sciences MDPI AG, 2012 14(2024), 2, p 539 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:14 year:2024 number:2, p 539 https://doi.org/10.3390/app14020539 kostenfrei https://doaj.org/article/bc7a602b126a452bba770f63f8a6f126 kostenfrei https://www.mdpi.com/2076-3417/14/2/539 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 14 2024 2, p 539
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source	In Applied Sciences 14(2024), 2, p 539 volume:14 year:2024 number:2, p 539
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topic_facet	transmission tower re-identification transmission tower detection YOLO triplet loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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authorswithroles_txt_mv	Lei Chen @@aut@@ Zuowei Yang @@aut@@ Fengyun Huang @@aut@@ Yiwei Dai @@aut@@ Rui Liu @@aut@@ Jiajia Li @@aut@@
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callnumber-first	T - Technology
author	Lei Chen
spellingShingle	Lei Chen misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc transmission tower re-identification misc transmission tower detection misc YOLO misc triplet loss misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Transmission Tower Re-Identification Algorithm Based on Machine Vision
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issn	20763417
topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Transmission Tower Re-Identification Algorithm Based on Machine Vision transmission tower re-identification transmission tower detection YOLO triplet loss
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc transmission tower re-identification misc transmission tower detection misc YOLO misc triplet loss 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 transmission tower re-identification misc transmission tower detection misc YOLO misc triplet loss 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 transmission tower re-identification misc transmission tower detection misc YOLO misc triplet loss misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Transmission Tower Re-Identification Algorithm Based on Machine Vision
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title_full	Transmission Tower Re-Identification Algorithm Based on Machine Vision
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author_browse	Lei Chen Zuowei Yang Fengyun Huang Yiwei Dai Rui Liu Jiajia Li
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class	TA1-2040 QH301-705.5 QC1-999 QD1-999
format_se	Elektronische Aufsätze
author-letter	Lei Chen
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title_sort	transmission tower re-identification algorithm based on machine vision
callnumber	TA1-2040
title_auth	Transmission Tower Re-Identification Algorithm Based on Machine Vision
abstract	Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%.
abstractGer	Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%.
abstract_unstemmed	Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%.
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container_issue	2, p 539
title_short	Transmission Tower Re-Identification Algorithm Based on Machine Vision
url	https://doi.org/10.3390/app14020539 https://doaj.org/article/bc7a602b126a452bba770f63f8a6f126 https://www.mdpi.com/2076-3417/14/2/539 https://doaj.org/toc/2076-3417
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author2	Zuowei Yang Fengyun Huang Yiwei Dai Rui Liu Jiajia Li
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