Fish abundance estimation from multi-beam sonar by improved MCNN

Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fi...
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Autor*in:	Feng, Yihan [verfasserIn] Wei, Yaoguang Sun, Shuo Liu, Jincun An, Dong Wang, Jia

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Aquaculture Fish abundance estimation Imaging sonar Deep learning

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Aquatic ecology - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1968, 57(2023), 4 vom: 08. März, Seite 895-911
Übergeordnetes Werk:	volume:57 ; year:2023 ; number:4 ; day:08 ; month:03 ; pages:895-911

Links:	Volltext

DOI / URN:	10.1007/s10452-023-10007-z

Katalog-ID:	SPR053458230

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520			\|a Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries.
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allfields	10.1007/s10452-023-10007-z doi (DE-627)SPR053458230 (SPR)s10452-023-10007-z-e DE-627 ger DE-627 rakwb eng Feng, Yihan verfasserin aut Fish abundance estimation from multi-beam sonar by improved MCNN 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries. Aquaculture (dpeaa)DE-He213 Fish abundance estimation (dpeaa)DE-He213 Imaging sonar (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wei, Yaoguang aut Sun, Shuo aut Liu, Jincun aut An, Dong aut Wang, Jia aut Enthalten in Aquatic ecology Dordrecht [u.a.] : Springer Science + Business Media B.V., 1968 57(2023), 4 vom: 08. März, Seite 895-911 (DE-627)302724257 (DE-600)1492493-6 1573-5125 nnns volume:57 year:2023 number:4 day:08 month:03 pages:895-911 https://dx.doi.org/10.1007/s10452-023-10007-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 4 08 03 895-911
spelling	10.1007/s10452-023-10007-z doi (DE-627)SPR053458230 (SPR)s10452-023-10007-z-e DE-627 ger DE-627 rakwb eng Feng, Yihan verfasserin aut Fish abundance estimation from multi-beam sonar by improved MCNN 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries. Aquaculture (dpeaa)DE-He213 Fish abundance estimation (dpeaa)DE-He213 Imaging sonar (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wei, Yaoguang aut Sun, Shuo aut Liu, Jincun aut An, Dong aut Wang, Jia aut Enthalten in Aquatic ecology Dordrecht [u.a.] : Springer Science + Business Media B.V., 1968 57(2023), 4 vom: 08. März, Seite 895-911 (DE-627)302724257 (DE-600)1492493-6 1573-5125 nnns volume:57 year:2023 number:4 day:08 month:03 pages:895-911 https://dx.doi.org/10.1007/s10452-023-10007-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 4 08 03 895-911
allfields_unstemmed	10.1007/s10452-023-10007-z doi (DE-627)SPR053458230 (SPR)s10452-023-10007-z-e DE-627 ger DE-627 rakwb eng Feng, Yihan verfasserin aut Fish abundance estimation from multi-beam sonar by improved MCNN 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries. Aquaculture (dpeaa)DE-He213 Fish abundance estimation (dpeaa)DE-He213 Imaging sonar (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wei, Yaoguang aut Sun, Shuo aut Liu, Jincun aut An, Dong aut Wang, Jia aut Enthalten in Aquatic ecology Dordrecht [u.a.] : Springer Science + Business Media B.V., 1968 57(2023), 4 vom: 08. März, Seite 895-911 (DE-627)302724257 (DE-600)1492493-6 1573-5125 nnns volume:57 year:2023 number:4 day:08 month:03 pages:895-911 https://dx.doi.org/10.1007/s10452-023-10007-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 4 08 03 895-911
allfieldsGer	10.1007/s10452-023-10007-z doi (DE-627)SPR053458230 (SPR)s10452-023-10007-z-e DE-627 ger DE-627 rakwb eng Feng, Yihan verfasserin aut Fish abundance estimation from multi-beam sonar by improved MCNN 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries. Aquaculture (dpeaa)DE-He213 Fish abundance estimation (dpeaa)DE-He213 Imaging sonar (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wei, Yaoguang aut Sun, Shuo aut Liu, Jincun aut An, Dong aut Wang, Jia aut Enthalten in Aquatic ecology Dordrecht [u.a.] : Springer Science + Business Media B.V., 1968 57(2023), 4 vom: 08. März, Seite 895-911 (DE-627)302724257 (DE-600)1492493-6 1573-5125 nnns volume:57 year:2023 number:4 day:08 month:03 pages:895-911 https://dx.doi.org/10.1007/s10452-023-10007-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 4 08 03 895-911
allfieldsSound	10.1007/s10452-023-10007-z doi (DE-627)SPR053458230 (SPR)s10452-023-10007-z-e DE-627 ger DE-627 rakwb eng Feng, Yihan verfasserin aut Fish abundance estimation from multi-beam sonar by improved MCNN 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries. Aquaculture (dpeaa)DE-He213 Fish abundance estimation (dpeaa)DE-He213 Imaging sonar (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wei, Yaoguang aut Sun, Shuo aut Liu, Jincun aut An, Dong aut Wang, Jia aut Enthalten in Aquatic ecology Dordrecht [u.a.] : Springer Science + Business Media B.V., 1968 57(2023), 4 vom: 08. März, Seite 895-911 (DE-627)302724257 (DE-600)1492493-6 1573-5125 nnns volume:57 year:2023 number:4 day:08 month:03 pages:895-911 https://dx.doi.org/10.1007/s10452-023-10007-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2023 4 08 03 895-911
language	English
source	Enthalten in Aquatic ecology 57(2023), 4 vom: 08. März, Seite 895-911 volume:57 year:2023 number:4 day:08 month:03 pages:895-911
sourceStr	Enthalten in Aquatic ecology 57(2023), 4 vom: 08. März, Seite 895-911 volume:57 year:2023 number:4 day:08 month:03 pages:895-911
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Aquaculture Fish abundance estimation Imaging sonar Deep learning
isfreeaccess_bool	false
container_title	Aquatic ecology
authorswithroles_txt_mv	Feng, Yihan @@aut@@ Wei, Yaoguang @@aut@@ Sun, Shuo @@aut@@ Liu, Jincun @@aut@@ An, Dong @@aut@@ Wang, Jia @@aut@@
publishDateDaySort_date	2023-03-08T00:00:00Z
hierarchy_top_id	302724257
id	SPR053458230
language_de	englisch
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author	Feng, Yihan
spellingShingle	Feng, Yihan misc Aquaculture misc Fish abundance estimation misc Imaging sonar misc Deep learning Fish abundance estimation from multi-beam sonar by improved MCNN
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topic_title	Fish abundance estimation from multi-beam sonar by improved MCNN Aquaculture (dpeaa)DE-He213 Fish abundance estimation (dpeaa)DE-He213 Imaging sonar (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213
topic	misc Aquaculture misc Fish abundance estimation misc Imaging sonar misc Deep learning
topic_unstemmed	misc Aquaculture misc Fish abundance estimation misc Imaging sonar misc Deep learning
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title	Fish abundance estimation from multi-beam sonar by improved MCNN
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title_full	Fish abundance estimation from multi-beam sonar by improved MCNN
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journal	Aquatic ecology
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author_browse	Feng, Yihan Wei, Yaoguang Sun, Shuo Liu, Jincun An, Dong Wang, Jia
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doi_str_mv	10.1007/s10452-023-10007-z
title_sort	fish abundance estimation from multi-beam sonar by improved mcnn
title_auth	Fish abundance estimation from multi-beam sonar by improved MCNN
abstract	Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Fish abundance estimation from multi-beam sonar by improved MCNN
url	https://dx.doi.org/10.1007/s10452-023-10007-z
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author2	Wei, Yaoguang Sun, Shuo Liu, Jincun An, Dong Wang, Jia
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up_date	2024-07-03T19:40:34.752Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053458230</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231020064705.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231020s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10452-023-10007-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053458230</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10452-023-10007-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Feng, Yihan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fish abundance estimation from multi-beam sonar by improved MCNN</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Aquatic products provide essential food and nutrients to humans, the abundance of fish is used in many aspects of aquaculture management, and it also undertakes a lot of tasks in the process of aquaculture, so it is a crucial link. This study introduces an automated method for estimating fish abundance in sonar images based on the modified MCNN (multi-column convolutional neural network), named FS-MCNN. We also proposed the multi-dilation rate fusion loss, which will improve the accuracy and robustness of the model. This method will improve the impact of low pixels in sonar images and blurry edges of target objects in sonar images. It further decreases the RMSE by 14.22% and the MAE by 11.83%, and the final accuracy is 92.83%. This study estimates fish abundance through imaging sonar, which will be able to reduce the effect of light and the complex environment, and it will also contribute to increase labor productivity, reduce the feed waste and enhance the level of information technology in aquaculture or fisheries.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aquaculture</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fish abundance estimation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imaging sonar</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wei, Yaoguang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Shuo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Jincun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">An, Dong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jia</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Aquatic ecology</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V., 1968</subfield><subfield code="g">57(2023), 4 vom: 08. 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