A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures

Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a...
Ausführliche Beschreibung

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Autor*in:	Huilin Ge [verfasserIn] Yuewei Dai [verfasserIn] Zhiyu Zhu [verfasserIn] Runbang Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	underwater imaging deep learning object detection image enhancement UW_YOLOv3

Übergeordnetes Werk:	In: Machines - MDPI AG, 2013, 10(2022), 9, p 809
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:9, p 809

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/machines10090809

Katalog-ID:	DOAJ084810718

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spelling	10.3390/machines10090809 doi (DE-627)DOAJ084810718 (DE-599)DOAJd314192c17b44e21b66bdc1e7f339af3 DE-627 ger DE-627 rakwb eng TJ1-1570 Huilin Ge verfasserin aut A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems. underwater imaging deep learning object detection image enhancement UW_YOLOv3 Mechanical engineering and machinery Yuewei Dai verfasserin aut Zhiyu Zhu verfasserin aut Runbang Liu verfasserin aut In Machines MDPI AG, 2013 10(2022), 9, p 809 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:10 year:2022 number:9, p 809 https://doi.org/10.3390/machines10090809 kostenfrei https://doaj.org/article/d314192c17b44e21b66bdc1e7f339af3 kostenfrei https://www.mdpi.com/2075-1702/10/9/809 kostenfrei https://doaj.org/toc/2075-1702 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 10 2022 9, p 809
allfields_unstemmed	10.3390/machines10090809 doi (DE-627)DOAJ084810718 (DE-599)DOAJd314192c17b44e21b66bdc1e7f339af3 DE-627 ger DE-627 rakwb eng TJ1-1570 Huilin Ge verfasserin aut A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems. underwater imaging deep learning object detection image enhancement UW_YOLOv3 Mechanical engineering and machinery Yuewei Dai verfasserin aut Zhiyu Zhu verfasserin aut Runbang Liu verfasserin aut In Machines MDPI AG, 2013 10(2022), 9, p 809 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:10 year:2022 number:9, p 809 https://doi.org/10.3390/machines10090809 kostenfrei https://doaj.org/article/d314192c17b44e21b66bdc1e7f339af3 kostenfrei https://www.mdpi.com/2075-1702/10/9/809 kostenfrei https://doaj.org/toc/2075-1702 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 10 2022 9, p 809
allfieldsGer	10.3390/machines10090809 doi (DE-627)DOAJ084810718 (DE-599)DOAJd314192c17b44e21b66bdc1e7f339af3 DE-627 ger DE-627 rakwb eng TJ1-1570 Huilin Ge verfasserin aut A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems. underwater imaging deep learning object detection image enhancement UW_YOLOv3 Mechanical engineering and machinery Yuewei Dai verfasserin aut Zhiyu Zhu verfasserin aut Runbang Liu verfasserin aut In Machines MDPI AG, 2013 10(2022), 9, p 809 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:10 year:2022 number:9, p 809 https://doi.org/10.3390/machines10090809 kostenfrei https://doaj.org/article/d314192c17b44e21b66bdc1e7f339af3 kostenfrei https://www.mdpi.com/2075-1702/10/9/809 kostenfrei https://doaj.org/toc/2075-1702 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 10 2022 9, p 809
allfieldsSound	10.3390/machines10090809 doi (DE-627)DOAJ084810718 (DE-599)DOAJd314192c17b44e21b66bdc1e7f339af3 DE-627 ger DE-627 rakwb eng TJ1-1570 Huilin Ge verfasserin aut A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems. underwater imaging deep learning object detection image enhancement UW_YOLOv3 Mechanical engineering and machinery Yuewei Dai verfasserin aut Zhiyu Zhu verfasserin aut Runbang Liu verfasserin aut In Machines MDPI AG, 2013 10(2022), 9, p 809 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:10 year:2022 number:9, p 809 https://doi.org/10.3390/machines10090809 kostenfrei https://doaj.org/article/d314192c17b44e21b66bdc1e7f339af3 kostenfrei https://www.mdpi.com/2075-1702/10/9/809 kostenfrei https://doaj.org/toc/2075-1702 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 10 2022 9, p 809
language	English
source	In Machines 10(2022), 9, p 809 volume:10 year:2022 number:9, p 809
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topic_facet	underwater imaging deep learning object detection image enhancement UW_YOLOv3 Mechanical engineering and machinery
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container_title	Machines
authorswithroles_txt_mv	Huilin Ge @@aut@@ Yuewei Dai @@aut@@ Zhiyu Zhu @@aut@@ Runbang Liu @@aut@@
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callnumber-first	T - Technology
author	Huilin Ge
spellingShingle	Huilin Ge misc TJ1-1570 misc underwater imaging misc deep learning misc object detection misc image enhancement misc UW_YOLOv3 misc Mechanical engineering and machinery A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures
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topic_title	TJ1-1570 A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures underwater imaging deep learning object detection image enhancement UW_YOLOv3
topic	misc TJ1-1570 misc underwater imaging misc deep learning misc object detection misc image enhancement misc UW_YOLOv3 misc Mechanical engineering and machinery
topic_unstemmed	misc TJ1-1570 misc underwater imaging misc deep learning misc object detection misc image enhancement misc UW_YOLOv3 misc Mechanical engineering and machinery
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title	A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures
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title_full	A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures
author_sort	Huilin Ge
journal	Machines
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lang_code	eng
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author_browse	Huilin Ge Yuewei Dai Zhiyu Zhu Runbang Liu
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author-letter	Huilin Ge
doi_str_mv	10.3390/machines10090809
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title_sort	deep learning model applied to optical image target detection and recognition for the identification of underwater biostructures
callnumber	TJ1-1570
title_auth	A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures
abstract	Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems.
abstractGer	Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems.
abstract_unstemmed	Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems.
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container_issue	9, p 809
title_short	A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures
url	https://doi.org/10.3390/machines10090809 https://doaj.org/article/d314192c17b44e21b66bdc1e7f339af3 https://www.mdpi.com/2075-1702/10/9/809 https://doaj.org/toc/2075-1702
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author2	Yuewei Dai Zhiyu Zhu Runbang Liu
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doi_str	10.3390/machines10090809
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up_date	2024-07-04T00:38:47.143Z
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A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures

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