Classification of foreign fibers using deep learning and its implementation on embedded system

In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digit...
Ausführliche Beschreibung

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Autor*in:	Wei Wei [verfasserIn] Dexiang Deng [verfasserIn] Lin Zeng [verfasserIn] Chen Zhang [verfasserIn] Wenxuan Shi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Übergeordnetes Werk:	In: International Journal of Advanced Robotic Systems - SAGE Publishing, 2008, 16(2019)
Übergeordnetes Werk:	volume:16 ; year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1177/1729881419867600

Katalog-ID:	DOAJ040634701

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520			\|a In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eight-core DSP) indicates the efficiency of the detection, which ensures the real-time application of the system.
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allfields	10.1177/1729881419867600 doi (DE-627)DOAJ040634701 (DE-599)DOAJ2226641e0af94d2db5633af37697d9e5 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Wei Wei verfasserin aut Classification of foreign fibers using deep learning and its implementation on embedded system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eight-core DSP) indicates the efficiency of the detection, which ensures the real-time application of the system. Electronics Electronic computers. Computer science Dexiang Deng verfasserin aut Lin Zeng verfasserin aut Chen Zhang verfasserin aut Wenxuan Shi verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 16(2019) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:16 year:2019 https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/article/2226641e0af94d2db5633af37697d9e5 kostenfrei https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/toc/1729-8814 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_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 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 16 2019
spelling	10.1177/1729881419867600 doi (DE-627)DOAJ040634701 (DE-599)DOAJ2226641e0af94d2db5633af37697d9e5 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Wei Wei verfasserin aut Classification of foreign fibers using deep learning and its implementation on embedded system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eight-core DSP) indicates the efficiency of the detection, which ensures the real-time application of the system. Electronics Electronic computers. Computer science Dexiang Deng verfasserin aut Lin Zeng verfasserin aut Chen Zhang verfasserin aut Wenxuan Shi verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 16(2019) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:16 year:2019 https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/article/2226641e0af94d2db5633af37697d9e5 kostenfrei https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/toc/1729-8814 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_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 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 16 2019
allfields_unstemmed	10.1177/1729881419867600 doi (DE-627)DOAJ040634701 (DE-599)DOAJ2226641e0af94d2db5633af37697d9e5 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Wei Wei verfasserin aut Classification of foreign fibers using deep learning and its implementation on embedded system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eight-core DSP) indicates the efficiency of the detection, which ensures the real-time application of the system. Electronics Electronic computers. Computer science Dexiang Deng verfasserin aut Lin Zeng verfasserin aut Chen Zhang verfasserin aut Wenxuan Shi verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 16(2019) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:16 year:2019 https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/article/2226641e0af94d2db5633af37697d9e5 kostenfrei https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/toc/1729-8814 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_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 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 16 2019
allfieldsGer	10.1177/1729881419867600 doi (DE-627)DOAJ040634701 (DE-599)DOAJ2226641e0af94d2db5633af37697d9e5 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Wei Wei verfasserin aut Classification of foreign fibers using deep learning and its implementation on embedded system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eight-core DSP) indicates the efficiency of the detection, which ensures the real-time application of the system. Electronics Electronic computers. Computer science Dexiang Deng verfasserin aut Lin Zeng verfasserin aut Chen Zhang verfasserin aut Wenxuan Shi verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 16(2019) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:16 year:2019 https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/article/2226641e0af94d2db5633af37697d9e5 kostenfrei https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/toc/1729-8814 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_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 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 16 2019
allfieldsSound	10.1177/1729881419867600 doi (DE-627)DOAJ040634701 (DE-599)DOAJ2226641e0af94d2db5633af37697d9e5 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Wei Wei verfasserin aut Classification of foreign fibers using deep learning and its implementation on embedded system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eight-core DSP) indicates the efficiency of the detection, which ensures the real-time application of the system. Electronics Electronic computers. Computer science Dexiang Deng verfasserin aut Lin Zeng verfasserin aut Chen Zhang verfasserin aut Wenxuan Shi verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 16(2019) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:16 year:2019 https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/article/2226641e0af94d2db5633af37697d9e5 kostenfrei https://doi.org/10.1177/1729881419867600 kostenfrei https://doaj.org/toc/1729-8814 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_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 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 16 2019
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callnumber-first	T - Technology
author	Wei Wei
spellingShingle	Wei Wei misc TK7800-8360 misc QA75.5-76.95 misc Electronics misc Electronic computers. Computer science Classification of foreign fibers using deep learning and its implementation on embedded system
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topic_title	TK7800-8360 QA75.5-76.95 Classification of foreign fibers using deep learning and its implementation on embedded system
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title	Classification of foreign fibers using deep learning and its implementation on embedded system
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title_auth	Classification of foreign fibers using deep learning and its implementation on embedded system
abstract	In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eight-core DSP) indicates the efficiency of the detection, which ensures the real-time application of the system.
abstractGer	In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eight-core DSP) indicates the efficiency of the detection, which ensures the real-time application of the system.
abstract_unstemmed	In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) + digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eight-core DSP) indicates the efficiency of the detection, which ensures the real-time application of the system.
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title_short	Classification of foreign fibers using deep learning and its implementation on embedded system
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Classification of foreign fibers using deep learning and its implementation on embedded system

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