An Unsafe Behavior Detection Method Based on Improved YOLO Framework

In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Bas...
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Autor*in:	Binbin Chen [verfasserIn] Xiuhui Wang [verfasserIn] Qifu Bao [verfasserIn] Bo Jia [verfasserIn] Xuesheng Li [verfasserIn] Yaru Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	behavior detection YOLO ASAE WFPN

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 11(2022), 12, p 1912
Übergeordnetes Werk:	volume:11 ; year:2022 ; number:12, p 1912

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics11121912

Katalog-ID:	DOAJ030166756

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spelling	10.3390/electronics11121912 doi (DE-627)DOAJ030166756 (DE-599)DOAJfe8eb384a10d47d0bd0ae57dc6055e7b DE-627 ger DE-627 rakwb eng TK7800-8360 Binbin Chen verfasserin aut An Unsafe Behavior Detection Method Based on Improved YOLO Framework 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%. behavior detection YOLO ASAE WFPN Electronics Xiuhui Wang verfasserin aut Qifu Bao verfasserin aut Bo Jia verfasserin aut Xuesheng Li verfasserin aut Yaru Wang verfasserin aut In Electronics MDPI AG, 2013 11(2022), 12, p 1912 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:11 year:2022 number:12, p 1912 https://doi.org/10.3390/electronics11121912 kostenfrei https://doaj.org/article/fe8eb384a10d47d0bd0ae57dc6055e7b kostenfrei https://www.mdpi.com/2079-9292/11/12/1912 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 11 2022 12, p 1912
allfields_unstemmed	10.3390/electronics11121912 doi (DE-627)DOAJ030166756 (DE-599)DOAJfe8eb384a10d47d0bd0ae57dc6055e7b DE-627 ger DE-627 rakwb eng TK7800-8360 Binbin Chen verfasserin aut An Unsafe Behavior Detection Method Based on Improved YOLO Framework 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%. behavior detection YOLO ASAE WFPN Electronics Xiuhui Wang verfasserin aut Qifu Bao verfasserin aut Bo Jia verfasserin aut Xuesheng Li verfasserin aut Yaru Wang verfasserin aut In Electronics MDPI AG, 2013 11(2022), 12, p 1912 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:11 year:2022 number:12, p 1912 https://doi.org/10.3390/electronics11121912 kostenfrei https://doaj.org/article/fe8eb384a10d47d0bd0ae57dc6055e7b kostenfrei https://www.mdpi.com/2079-9292/11/12/1912 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 11 2022 12, p 1912
allfieldsGer	10.3390/electronics11121912 doi (DE-627)DOAJ030166756 (DE-599)DOAJfe8eb384a10d47d0bd0ae57dc6055e7b DE-627 ger DE-627 rakwb eng TK7800-8360 Binbin Chen verfasserin aut An Unsafe Behavior Detection Method Based on Improved YOLO Framework 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%. behavior detection YOLO ASAE WFPN Electronics Xiuhui Wang verfasserin aut Qifu Bao verfasserin aut Bo Jia verfasserin aut Xuesheng Li verfasserin aut Yaru Wang verfasserin aut In Electronics MDPI AG, 2013 11(2022), 12, p 1912 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:11 year:2022 number:12, p 1912 https://doi.org/10.3390/electronics11121912 kostenfrei https://doaj.org/article/fe8eb384a10d47d0bd0ae57dc6055e7b kostenfrei https://www.mdpi.com/2079-9292/11/12/1912 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 11 2022 12, p 1912
allfieldsSound	10.3390/electronics11121912 doi (DE-627)DOAJ030166756 (DE-599)DOAJfe8eb384a10d47d0bd0ae57dc6055e7b DE-627 ger DE-627 rakwb eng TK7800-8360 Binbin Chen verfasserin aut An Unsafe Behavior Detection Method Based on Improved YOLO Framework 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%. behavior detection YOLO ASAE WFPN Electronics Xiuhui Wang verfasserin aut Qifu Bao verfasserin aut Bo Jia verfasserin aut Xuesheng Li verfasserin aut Yaru Wang verfasserin aut In Electronics MDPI AG, 2013 11(2022), 12, p 1912 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:11 year:2022 number:12, p 1912 https://doi.org/10.3390/electronics11121912 kostenfrei https://doaj.org/article/fe8eb384a10d47d0bd0ae57dc6055e7b kostenfrei https://www.mdpi.com/2079-9292/11/12/1912 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 11 2022 12, p 1912
language	English
source	In Electronics 11(2022), 12, p 1912 volume:11 year:2022 number:12, p 1912
sourceStr	In Electronics 11(2022), 12, p 1912 volume:11 year:2022 number:12, p 1912
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	behavior detection YOLO ASAE WFPN Electronics
isfreeaccess_bool	true
container_title	Electronics
authorswithroles_txt_mv	Binbin Chen @@aut@@ Xiuhui Wang @@aut@@ Qifu Bao @@aut@@ Bo Jia @@aut@@ Xuesheng Li @@aut@@ Yaru Wang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Binbin Chen
spellingShingle	Binbin Chen misc TK7800-8360 misc behavior detection misc YOLO misc ASAE misc WFPN misc Electronics An Unsafe Behavior Detection Method Based on Improved YOLO Framework
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topic_title	TK7800-8360 An Unsafe Behavior Detection Method Based on Improved YOLO Framework behavior detection YOLO ASAE WFPN
topic	misc TK7800-8360 misc behavior detection misc YOLO misc ASAE misc WFPN misc Electronics
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title	An Unsafe Behavior Detection Method Based on Improved YOLO Framework
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title_full	An Unsafe Behavior Detection Method Based on Improved YOLO Framework
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title_sort	unsafe behavior detection method based on improved yolo framework
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title_auth	An Unsafe Behavior Detection Method Based on Improved YOLO Framework
abstract	In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%.
abstractGer	In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%.
abstract_unstemmed	In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%.
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title_short	An Unsafe Behavior Detection Method Based on Improved YOLO Framework
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