Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM

The belt surface of the mine belt conveyor can cause serious wear under the condition of long-term high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety, and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex-YOLOv8-EfficientNet-NAM (RYEN algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and can maximize the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RYEN algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RYEN algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RYEN algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RYEN algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lijie Yang [verfasserIn] Guangyu Chen [verfasserIn] Jiehui Liu [verfasserIn] Jinxi Guo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Conveyor belt wear inspection Retinex-YOLOv8-EfficientNet-NAM machine vision deep learning YOLOv8

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 12(2024), Seite 25309-25324
Übergeordnetes Werk:	volume:12 ; year:2024 ; pages:25309-25324

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2024.3363834

Katalog-ID:	DOAJ100324738

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allfields	10.1109/ACCESS.2024.3363834 doi (DE-627)DOAJ100324738 (DE-599)DOAJd36affce6f284170949fac2f40146350 DE-627 ger DE-627 rakwb eng TK1-9971 Lijie Yang verfasserin aut Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belt surface of the mine belt conveyor can cause serious wear under the condition of long-term high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety, and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex-YOLOv8-EfficientNet-NAM (RYEN algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and can maximize the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RYEN algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RYEN algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RYEN algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RYEN algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy. Conveyor belt wear inspection Retinex-YOLOv8-EfficientNet-NAM machine vision deep learning YOLOv8 Electrical engineering. Electronics. Nuclear engineering Guangyu Chen verfasserin aut Jiehui Liu verfasserin aut Jinxi Guo verfasserin aut In IEEE Access IEEE, 2014 12(2024), Seite 25309-25324 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:12 year:2024 pages:25309-25324 https://doi.org/10.1109/ACCESS.2024.3363834 kostenfrei https://doaj.org/article/d36affce6f284170949fac2f40146350 kostenfrei https://ieeexplore.ieee.org/document/10428009/ 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 12 2024 25309-25324
spelling	10.1109/ACCESS.2024.3363834 doi (DE-627)DOAJ100324738 (DE-599)DOAJd36affce6f284170949fac2f40146350 DE-627 ger DE-627 rakwb eng TK1-9971 Lijie Yang verfasserin aut Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belt surface of the mine belt conveyor can cause serious wear under the condition of long-term high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety, and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex-YOLOv8-EfficientNet-NAM (RYEN algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and can maximize the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RYEN algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RYEN algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RYEN algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RYEN algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy. Conveyor belt wear inspection Retinex-YOLOv8-EfficientNet-NAM machine vision deep learning YOLOv8 Electrical engineering. Electronics. Nuclear engineering Guangyu Chen verfasserin aut Jiehui Liu verfasserin aut Jinxi Guo verfasserin aut In IEEE Access IEEE, 2014 12(2024), Seite 25309-25324 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:12 year:2024 pages:25309-25324 https://doi.org/10.1109/ACCESS.2024.3363834 kostenfrei https://doaj.org/article/d36affce6f284170949fac2f40146350 kostenfrei https://ieeexplore.ieee.org/document/10428009/ 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 12 2024 25309-25324
allfields_unstemmed	10.1109/ACCESS.2024.3363834 doi (DE-627)DOAJ100324738 (DE-599)DOAJd36affce6f284170949fac2f40146350 DE-627 ger DE-627 rakwb eng TK1-9971 Lijie Yang verfasserin aut Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belt surface of the mine belt conveyor can cause serious wear under the condition of long-term high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety, and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex-YOLOv8-EfficientNet-NAM (RYEN algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and can maximize the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RYEN algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RYEN algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RYEN algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RYEN algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy. Conveyor belt wear inspection Retinex-YOLOv8-EfficientNet-NAM machine vision deep learning YOLOv8 Electrical engineering. Electronics. Nuclear engineering Guangyu Chen verfasserin aut Jiehui Liu verfasserin aut Jinxi Guo verfasserin aut In IEEE Access IEEE, 2014 12(2024), Seite 25309-25324 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:12 year:2024 pages:25309-25324 https://doi.org/10.1109/ACCESS.2024.3363834 kostenfrei https://doaj.org/article/d36affce6f284170949fac2f40146350 kostenfrei https://ieeexplore.ieee.org/document/10428009/ 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 12 2024 25309-25324
allfieldsGer	10.1109/ACCESS.2024.3363834 doi (DE-627)DOAJ100324738 (DE-599)DOAJd36affce6f284170949fac2f40146350 DE-627 ger DE-627 rakwb eng TK1-9971 Lijie Yang verfasserin aut Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belt surface of the mine belt conveyor can cause serious wear under the condition of long-term high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety, and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex-YOLOv8-EfficientNet-NAM (RYEN algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and can maximize the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RYEN algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RYEN algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RYEN algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RYEN algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy. Conveyor belt wear inspection Retinex-YOLOv8-EfficientNet-NAM machine vision deep learning YOLOv8 Electrical engineering. Electronics. Nuclear engineering Guangyu Chen verfasserin aut Jiehui Liu verfasserin aut Jinxi Guo verfasserin aut In IEEE Access IEEE, 2014 12(2024), Seite 25309-25324 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:12 year:2024 pages:25309-25324 https://doi.org/10.1109/ACCESS.2024.3363834 kostenfrei https://doaj.org/article/d36affce6f284170949fac2f40146350 kostenfrei https://ieeexplore.ieee.org/document/10428009/ 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 12 2024 25309-25324
allfieldsSound	10.1109/ACCESS.2024.3363834 doi (DE-627)DOAJ100324738 (DE-599)DOAJd36affce6f284170949fac2f40146350 DE-627 ger DE-627 rakwb eng TK1-9971 Lijie Yang verfasserin aut Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belt surface of the mine belt conveyor can cause serious wear under the condition of long-term high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety, and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex-YOLOv8-EfficientNet-NAM (RYEN algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and can maximize the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RYEN algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RYEN algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RYEN algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RYEN algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy. Conveyor belt wear inspection Retinex-YOLOv8-EfficientNet-NAM machine vision deep learning YOLOv8 Electrical engineering. Electronics. Nuclear engineering Guangyu Chen verfasserin aut Jiehui Liu verfasserin aut Jinxi Guo verfasserin aut In IEEE Access IEEE, 2014 12(2024), Seite 25309-25324 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:12 year:2024 pages:25309-25324 https://doi.org/10.1109/ACCESS.2024.3363834 kostenfrei https://doaj.org/article/d36affce6f284170949fac2f40146350 kostenfrei https://ieeexplore.ieee.org/document/10428009/ 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 12 2024 25309-25324
language	English
source	In IEEE Access 12(2024), Seite 25309-25324 volume:12 year:2024 pages:25309-25324
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topic_facet	Conveyor belt wear inspection Retinex-YOLOv8-EfficientNet-NAM machine vision deep learning YOLOv8 Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Lijie Yang @@aut@@ Guangyu Chen @@aut@@ Jiehui Liu @@aut@@ Jinxi Guo @@aut@@
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callnumber-first	T - Technology
author	Lijie Yang
spellingShingle	Lijie Yang misc TK1-9971 misc Conveyor belt wear inspection misc Retinex-YOLOv8-EfficientNet-NAM misc machine vision misc deep learning misc YOLOv8 misc Electrical engineering. Electronics. Nuclear engineering Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM
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topic_title	TK1-9971 Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM Conveyor belt wear inspection Retinex-YOLOv8-EfficientNet-NAM machine vision deep learning YOLOv8
topic	misc TK1-9971 misc Conveyor belt wear inspection misc Retinex-YOLOv8-EfficientNet-NAM misc machine vision misc deep learning misc YOLOv8 misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Conveyor belt wear inspection misc Retinex-YOLOv8-EfficientNet-NAM misc machine vision misc deep learning misc YOLOv8 misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Conveyor belt wear inspection misc Retinex-YOLOv8-EfficientNet-NAM misc machine vision misc deep learning misc YOLOv8 misc Electrical engineering. Electronics. Nuclear engineering
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM
ctrlnum	(DE-627)DOAJ100324738 (DE-599)DOAJd36affce6f284170949fac2f40146350
title_full	Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM
author_sort	Lijie Yang
journal	IEEE Access
journalStr	IEEE Access
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publishDateSort	2024
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author_browse	Lijie Yang Guangyu Chen Jiehui Liu Jinxi Guo
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class	TK1-9971
format_se	Elektronische Aufsätze
author-letter	Lijie Yang
doi_str_mv	10.1109/ACCESS.2024.3363834
author2-role	verfasserin
title_sort	wear state detection of conveyor belt in underground mine based on retinex- yolov8-efficientnet-nam
callnumber	TK1-9971
title_auth	Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM
abstract	The belt surface of the mine belt conveyor can cause serious wear under the condition of long-term high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety, and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex-YOLOv8-EfficientNet-NAM (RYEN algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and can maximize the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RYEN algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RYEN algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RYEN algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RYEN algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy.
abstractGer	The belt surface of the mine belt conveyor can cause serious wear under the condition of long-term high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety, and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex-YOLOv8-EfficientNet-NAM (RYEN algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and can maximize the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RYEN algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RYEN algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RYEN algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RYEN algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy.
abstract_unstemmed	The belt surface of the mine belt conveyor can cause serious wear under the condition of long-term high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety, and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex-YOLOv8-EfficientNet-NAM (RYEN algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and can maximize the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RYEN algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RYEN algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RYEN algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RYEN algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy.
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title_short	Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM
url	https://doi.org/10.1109/ACCESS.2024.3363834 https://doaj.org/article/d36affce6f284170949fac2f40146350 https://ieeexplore.ieee.org/document/10428009/ https://doaj.org/toc/2169-3536
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