Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion

In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and...
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Autor*in:	Haodong Wang [verfasserIn] Jun Xie [verfasserIn] Xinying Xu [verfasserIn] Zihao Zheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	PCB defect detection few-shot learning feature enhancement multi-scale fusion

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 129911-129924
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:129911-129924

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3228392

Katalog-ID:	DOAJ083300805

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520			\|a In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula< <tex-math notation="LaTeX"<$\text{k}=1$ </tex-math<</inline-formula<,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value.
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allfields	10.1109/ACCESS.2022.3228392 doi (DE-627)DOAJ083300805 (DE-599)DOAJ5456fed2772c4886ba7d72913ad12d15 DE-627 ger DE-627 rakwb eng TK1-9971 Haodong Wang verfasserin aut Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula< <tex-math notation="LaTeX"<$\text{k}=1$ </tex-math<</inline-formula<,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value. PCB defect detection few-shot learning feature enhancement multi-scale fusion Electrical engineering. Electronics. Nuclear engineering Jun Xie verfasserin aut Xinying Xu verfasserin aut Zihao Zheng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 129911-129924 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:129911-129924 https://doi.org/10.1109/ACCESS.2022.3228392 kostenfrei https://doaj.org/article/5456fed2772c4886ba7d72913ad12d15 kostenfrei https://ieeexplore.ieee.org/document/9979794/ kostenfrei https://doaj.org/toc/2169-3536 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_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 10 2022 129911-129924
spelling	10.1109/ACCESS.2022.3228392 doi (DE-627)DOAJ083300805 (DE-599)DOAJ5456fed2772c4886ba7d72913ad12d15 DE-627 ger DE-627 rakwb eng TK1-9971 Haodong Wang verfasserin aut Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula< <tex-math notation="LaTeX"<$\text{k}=1$ </tex-math<</inline-formula<,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value. PCB defect detection few-shot learning feature enhancement multi-scale fusion Electrical engineering. Electronics. Nuclear engineering Jun Xie verfasserin aut Xinying Xu verfasserin aut Zihao Zheng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 129911-129924 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:129911-129924 https://doi.org/10.1109/ACCESS.2022.3228392 kostenfrei https://doaj.org/article/5456fed2772c4886ba7d72913ad12d15 kostenfrei https://ieeexplore.ieee.org/document/9979794/ kostenfrei https://doaj.org/toc/2169-3536 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_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 10 2022 129911-129924
allfields_unstemmed	10.1109/ACCESS.2022.3228392 doi (DE-627)DOAJ083300805 (DE-599)DOAJ5456fed2772c4886ba7d72913ad12d15 DE-627 ger DE-627 rakwb eng TK1-9971 Haodong Wang verfasserin aut Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula< <tex-math notation="LaTeX"<$\text{k}=1$ </tex-math<</inline-formula<,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value. PCB defect detection few-shot learning feature enhancement multi-scale fusion Electrical engineering. Electronics. Nuclear engineering Jun Xie verfasserin aut Xinying Xu verfasserin aut Zihao Zheng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 129911-129924 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:129911-129924 https://doi.org/10.1109/ACCESS.2022.3228392 kostenfrei https://doaj.org/article/5456fed2772c4886ba7d72913ad12d15 kostenfrei https://ieeexplore.ieee.org/document/9979794/ kostenfrei https://doaj.org/toc/2169-3536 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_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 10 2022 129911-129924
allfieldsGer	10.1109/ACCESS.2022.3228392 doi (DE-627)DOAJ083300805 (DE-599)DOAJ5456fed2772c4886ba7d72913ad12d15 DE-627 ger DE-627 rakwb eng TK1-9971 Haodong Wang verfasserin aut Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula< <tex-math notation="LaTeX"<$\text{k}=1$ </tex-math<</inline-formula<,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value. PCB defect detection few-shot learning feature enhancement multi-scale fusion Electrical engineering. Electronics. Nuclear engineering Jun Xie verfasserin aut Xinying Xu verfasserin aut Zihao Zheng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 129911-129924 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:129911-129924 https://doi.org/10.1109/ACCESS.2022.3228392 kostenfrei https://doaj.org/article/5456fed2772c4886ba7d72913ad12d15 kostenfrei https://ieeexplore.ieee.org/document/9979794/ kostenfrei https://doaj.org/toc/2169-3536 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_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 10 2022 129911-129924
allfieldsSound	10.1109/ACCESS.2022.3228392 doi (DE-627)DOAJ083300805 (DE-599)DOAJ5456fed2772c4886ba7d72913ad12d15 DE-627 ger DE-627 rakwb eng TK1-9971 Haodong Wang verfasserin aut Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula< <tex-math notation="LaTeX"<$\text{k}=1$ </tex-math<</inline-formula<,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value. PCB defect detection few-shot learning feature enhancement multi-scale fusion Electrical engineering. Electronics. Nuclear engineering Jun Xie verfasserin aut Xinying Xu verfasserin aut Zihao Zheng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 129911-129924 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:129911-129924 https://doi.org/10.1109/ACCESS.2022.3228392 kostenfrei https://doaj.org/article/5456fed2772c4886ba7d72913ad12d15 kostenfrei https://ieeexplore.ieee.org/document/9979794/ kostenfrei https://doaj.org/toc/2169-3536 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_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 10 2022 129911-129924
language	English
source	In IEEE Access 10(2022), Seite 129911-129924 volume:10 year:2022 pages:129911-129924
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authorswithroles_txt_mv	Haodong Wang @@aut@@ Jun Xie @@aut@@ Xinying Xu @@aut@@ Zihao Zheng @@aut@@
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callnumber-first	T - Technology
author	Haodong Wang
spellingShingle	Haodong Wang misc TK1-9971 misc PCB defect detection misc few-shot learning misc feature enhancement misc multi-scale fusion misc Electrical engineering. Electronics. Nuclear engineering Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion
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topic_title	TK1-9971 Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion PCB defect detection few-shot learning feature enhancement multi-scale fusion
topic	misc TK1-9971 misc PCB defect detection misc few-shot learning misc feature enhancement misc multi-scale fusion misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc PCB defect detection misc few-shot learning misc feature enhancement misc multi-scale fusion misc Electrical engineering. Electronics. Nuclear engineering
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title	Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion
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title_full	Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion
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title_sort	few-shot pcb surface defect detection based on feature enhancement and multi-scale fusion
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title_auth	Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion
abstract	In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula< <tex-math notation="LaTeX"<$\text{k}=1$ </tex-math<</inline-formula<,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value.
abstractGer	In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula< <tex-math notation="LaTeX"<$\text{k}=1$ </tex-math<</inline-formula<,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value.
abstract_unstemmed	In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula< <tex-math notation="LaTeX"<$\text{k}=1$ </tex-math<</inline-formula<,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value.
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title_short	Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion
url	https://doi.org/10.1109/ACCESS.2022.3228392 https://doaj.org/article/5456fed2772c4886ba7d72913ad12d15 https://ieeexplore.ieee.org/document/9979794/ https://doaj.org/toc/2169-3536
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doi_str	10.1109/ACCESS.2022.3228392
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up_date	2024-07-03T16:42:03.051Z
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