Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion

Abstract Intelligent recognition of assembly behaviors of workshop production personnel is crucial to improve production assembly efficiency and ensure production safety. This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-...
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Autor*in:	Chengjun Chen [verfasserIn] Xicong Zhao [verfasserIn] Jinlei Wang [verfasserIn] Dongnian Li [verfasserIn] Yuanlin Guan [verfasserIn] Jun Hong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Scientific Reports - Nature Portfolio, 2011, 12(2022), 1, Seite 13
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:1 ; pages:13

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1038/s41598-022-11206-8

Katalog-ID:	DOAJ02806254X

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520			\|a Abstract Intelligent recognition of assembly behaviors of workshop production personnel is crucial to improve production assembly efficiency and ensure production safety. This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. The proposed model learns the potential relationship between assembly actions and assembly tools for recognizing assembly behaviors. Meanwhile, the introduction of an attention mechanism helps the network to focus on the key information in assembly behavior images. Besides, the multi-scale feature fusion module is introduced to enable the network to better extract image features at different scales. This paper constructs a data set containing 15 types of workshop production behaviors, and the proposed assembly behavior recognition model is tested on this data set. The experimental results show that the proposed model achieves good recognition results, with an average assembly recognition accuracy of 93.1%.
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allfields	10.1038/s41598-022-11206-8 doi (DE-627)DOAJ02806254X (DE-599)DOAJaf43ff5221674015841d4c9252cafea6 DE-627 ger DE-627 rakwb eng Chengjun Chen verfasserin aut Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intelligent recognition of assembly behaviors of workshop production personnel is crucial to improve production assembly efficiency and ensure production safety. This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. The proposed model learns the potential relationship between assembly actions and assembly tools for recognizing assembly behaviors. Meanwhile, the introduction of an attention mechanism helps the network to focus on the key information in assembly behavior images. Besides, the multi-scale feature fusion module is introduced to enable the network to better extract image features at different scales. This paper constructs a data set containing 15 types of workshop production behaviors, and the proposed assembly behavior recognition model is tested on this data set. The experimental results show that the proposed model achieves good recognition results, with an average assembly recognition accuracy of 93.1%. Medicine R Science Q Xicong Zhao verfasserin aut Jinlei Wang verfasserin aut Dongnian Li verfasserin aut Yuanlin Guan verfasserin aut Jun Hong verfasserin aut In Scientific Reports Nature Portfolio, 2011 12(2022), 1, Seite 13 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:12 year:2022 number:1 pages:13 https://doi.org/10.1038/s41598-022-11206-8 kostenfrei https://doaj.org/article/af43ff5221674015841d4c9252cafea6 kostenfrei https://doi.org/10.1038/s41598-022-11206-8 kostenfrei https://doaj.org/toc/2045-2322 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_31 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 1 13
spelling	10.1038/s41598-022-11206-8 doi (DE-627)DOAJ02806254X (DE-599)DOAJaf43ff5221674015841d4c9252cafea6 DE-627 ger DE-627 rakwb eng Chengjun Chen verfasserin aut Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intelligent recognition of assembly behaviors of workshop production personnel is crucial to improve production assembly efficiency and ensure production safety. This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. The proposed model learns the potential relationship between assembly actions and assembly tools for recognizing assembly behaviors. Meanwhile, the introduction of an attention mechanism helps the network to focus on the key information in assembly behavior images. Besides, the multi-scale feature fusion module is introduced to enable the network to better extract image features at different scales. This paper constructs a data set containing 15 types of workshop production behaviors, and the proposed assembly behavior recognition model is tested on this data set. The experimental results show that the proposed model achieves good recognition results, with an average assembly recognition accuracy of 93.1%. Medicine R Science Q Xicong Zhao verfasserin aut Jinlei Wang verfasserin aut Dongnian Li verfasserin aut Yuanlin Guan verfasserin aut Jun Hong verfasserin aut In Scientific Reports Nature Portfolio, 2011 12(2022), 1, Seite 13 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:12 year:2022 number:1 pages:13 https://doi.org/10.1038/s41598-022-11206-8 kostenfrei https://doaj.org/article/af43ff5221674015841d4c9252cafea6 kostenfrei https://doi.org/10.1038/s41598-022-11206-8 kostenfrei https://doaj.org/toc/2045-2322 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_31 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 1 13
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author	Chengjun Chen
spellingShingle	Chengjun Chen misc Medicine misc R misc Science misc Q Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion
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topic_title	Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion
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title	Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion
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title_sort	dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion
title_auth	Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion
abstract	Abstract Intelligent recognition of assembly behaviors of workshop production personnel is crucial to improve production assembly efficiency and ensure production safety. This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. The proposed model learns the potential relationship between assembly actions and assembly tools for recognizing assembly behaviors. Meanwhile, the introduction of an attention mechanism helps the network to focus on the key information in assembly behavior images. Besides, the multi-scale feature fusion module is introduced to enable the network to better extract image features at different scales. This paper constructs a data set containing 15 types of workshop production behaviors, and the proposed assembly behavior recognition model is tested on this data set. The experimental results show that the proposed model achieves good recognition results, with an average assembly recognition accuracy of 93.1%.
abstractGer	Abstract Intelligent recognition of assembly behaviors of workshop production personnel is crucial to improve production assembly efficiency and ensure production safety. This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. The proposed model learns the potential relationship between assembly actions and assembly tools for recognizing assembly behaviors. Meanwhile, the introduction of an attention mechanism helps the network to focus on the key information in assembly behavior images. Besides, the multi-scale feature fusion module is introduced to enable the network to better extract image features at different scales. This paper constructs a data set containing 15 types of workshop production behaviors, and the proposed assembly behavior recognition model is tested on this data set. The experimental results show that the proposed model achieves good recognition results, with an average assembly recognition accuracy of 93.1%.
abstract_unstemmed	Abstract Intelligent recognition of assembly behaviors of workshop production personnel is crucial to improve production assembly efficiency and ensure production safety. This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. The proposed model learns the potential relationship between assembly actions and assembly tools for recognizing assembly behaviors. Meanwhile, the introduction of an attention mechanism helps the network to focus on the key information in assembly behavior images. Besides, the multi-scale feature fusion module is introduced to enable the network to better extract image features at different scales. This paper constructs a data set containing 15 types of workshop production behaviors, and the proposed assembly behavior recognition model is tested on this data set. The experimental results show that the proposed model achieves good recognition results, with an average assembly recognition accuracy of 93.1%.
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title_short	Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion
url	https://doi.org/10.1038/s41598-022-11206-8 https://doaj.org/article/af43ff5221674015841d4c9252cafea6 https://doaj.org/toc/2045-2322
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Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion

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