A new image dataset for the evaluation of automatic fingerlings counting

Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings s...
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Autor*in:	Garcia, Vanir [verfasserIn] Sant’Ana, Diego André [verfasserIn] Garcia Zanoni, Vanda Alice [verfasserIn] Brito Pache, Marcio Carneiro [verfasserIn] Naka, Marco Hiroshi [verfasserIn] França Albuquerque, Pedro Lucas [verfasserIn] Lewandowski, Tiago [verfasserIn] Silva Oliveira Junior, Adair Da [verfasserIn] Araújo Rozales, João Victor [verfasserIn] Ferreira, Milena Wolff [verfasserIn] de Queiroz, Eduardo Quirino Arguelho [verfasserIn] Marino Almanza, José Carlos [verfasserIn] Pistori, Hemerson [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Fingerling counter Dataset Pisciculture Fingerlings Computer vision

Übergeordnetes Werk:	Enthalten in: Aquacultural engineering - Amsterdam [u.a.] : Elsevier Science, 1982, 89
Übergeordnetes Werk:	volume:89

DOI / URN:	10.1016/j.aquaeng.2020.102064

Katalog-ID:	ELV003917827

Internformat


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520			\|a Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers.
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700	1		\|a Marino Almanza, José Carlos \|e verfasserin \|4 aut
700	1		\|a Pistori, Hemerson \|e verfasserin \|4 aut
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allfields	10.1016/j.aquaeng.2020.102064 doi (DE-627)ELV003917827 (ELSEVIER)S0144-8609(19)30170-0 DE-627 ger DE-627 rda eng 550 690 DE-600 48.68 bkl Garcia, Vanir verfasserin aut A new image dataset for the evaluation of automatic fingerlings counting 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers. Fingerling counter Dataset Pisciculture Fingerlings Computer vision Sant’Ana, Diego André verfasserin aut Garcia Zanoni, Vanda Alice verfasserin aut Brito Pache, Marcio Carneiro verfasserin aut Naka, Marco Hiroshi verfasserin aut França Albuquerque, Pedro Lucas verfasserin aut Lewandowski, Tiago verfasserin aut Silva Oliveira Junior, Adair Da verfasserin aut Araújo Rozales, João Victor verfasserin aut Ferreira, Milena Wolff verfasserin aut de Queiroz, Eduardo Quirino Arguelho verfasserin aut Marino Almanza, José Carlos verfasserin aut Pistori, Hemerson verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 89 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur AR 89
spelling	10.1016/j.aquaeng.2020.102064 doi (DE-627)ELV003917827 (ELSEVIER)S0144-8609(19)30170-0 DE-627 ger DE-627 rda eng 550 690 DE-600 48.68 bkl Garcia, Vanir verfasserin aut A new image dataset for the evaluation of automatic fingerlings counting 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers. Fingerling counter Dataset Pisciculture Fingerlings Computer vision Sant’Ana, Diego André verfasserin aut Garcia Zanoni, Vanda Alice verfasserin aut Brito Pache, Marcio Carneiro verfasserin aut Naka, Marco Hiroshi verfasserin aut França Albuquerque, Pedro Lucas verfasserin aut Lewandowski, Tiago verfasserin aut Silva Oliveira Junior, Adair Da verfasserin aut Araújo Rozales, João Victor verfasserin aut Ferreira, Milena Wolff verfasserin aut de Queiroz, Eduardo Quirino Arguelho verfasserin aut Marino Almanza, José Carlos verfasserin aut Pistori, Hemerson verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 89 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur AR 89
allfields_unstemmed	10.1016/j.aquaeng.2020.102064 doi (DE-627)ELV003917827 (ELSEVIER)S0144-8609(19)30170-0 DE-627 ger DE-627 rda eng 550 690 DE-600 48.68 bkl Garcia, Vanir verfasserin aut A new image dataset for the evaluation of automatic fingerlings counting 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers. Fingerling counter Dataset Pisciculture Fingerlings Computer vision Sant’Ana, Diego André verfasserin aut Garcia Zanoni, Vanda Alice verfasserin aut Brito Pache, Marcio Carneiro verfasserin aut Naka, Marco Hiroshi verfasserin aut França Albuquerque, Pedro Lucas verfasserin aut Lewandowski, Tiago verfasserin aut Silva Oliveira Junior, Adair Da verfasserin aut Araújo Rozales, João Victor verfasserin aut Ferreira, Milena Wolff verfasserin aut de Queiroz, Eduardo Quirino Arguelho verfasserin aut Marino Almanza, José Carlos verfasserin aut Pistori, Hemerson verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 89 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur AR 89
allfieldsGer	10.1016/j.aquaeng.2020.102064 doi (DE-627)ELV003917827 (ELSEVIER)S0144-8609(19)30170-0 DE-627 ger DE-627 rda eng 550 690 DE-600 48.68 bkl Garcia, Vanir verfasserin aut A new image dataset for the evaluation of automatic fingerlings counting 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers. Fingerling counter Dataset Pisciculture Fingerlings Computer vision Sant’Ana, Diego André verfasserin aut Garcia Zanoni, Vanda Alice verfasserin aut Brito Pache, Marcio Carneiro verfasserin aut Naka, Marco Hiroshi verfasserin aut França Albuquerque, Pedro Lucas verfasserin aut Lewandowski, Tiago verfasserin aut Silva Oliveira Junior, Adair Da verfasserin aut Araújo Rozales, João Victor verfasserin aut Ferreira, Milena Wolff verfasserin aut de Queiroz, Eduardo Quirino Arguelho verfasserin aut Marino Almanza, José Carlos verfasserin aut Pistori, Hemerson verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 89 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur AR 89
allfieldsSound	10.1016/j.aquaeng.2020.102064 doi (DE-627)ELV003917827 (ELSEVIER)S0144-8609(19)30170-0 DE-627 ger DE-627 rda eng 550 690 DE-600 48.68 bkl Garcia, Vanir verfasserin aut A new image dataset for the evaluation of automatic fingerlings counting 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers. Fingerling counter Dataset Pisciculture Fingerlings Computer vision Sant’Ana, Diego André verfasserin aut Garcia Zanoni, Vanda Alice verfasserin aut Brito Pache, Marcio Carneiro verfasserin aut Naka, Marco Hiroshi verfasserin aut França Albuquerque, Pedro Lucas verfasserin aut Lewandowski, Tiago verfasserin aut Silva Oliveira Junior, Adair Da verfasserin aut Araújo Rozales, João Victor verfasserin aut Ferreira, Milena Wolff verfasserin aut de Queiroz, Eduardo Quirino Arguelho verfasserin aut Marino Almanza, José Carlos verfasserin aut Pistori, Hemerson verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 89 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur AR 89
language	English
source	Enthalten in Aquacultural engineering 89 volume:89
sourceStr	Enthalten in Aquacultural engineering 89 volume:89
format_phy_str_mv	Article
bklname	Aquakultur
institution	findex.gbv.de
topic_facet	Fingerling counter Dataset Pisciculture Fingerlings Computer vision
dewey-raw	550
isfreeaccess_bool	false
container_title	Aquacultural engineering
authorswithroles_txt_mv	Garcia, Vanir @@aut@@ Sant’Ana, Diego André @@aut@@ Garcia Zanoni, Vanda Alice @@aut@@ Brito Pache, Marcio Carneiro @@aut@@ Naka, Marco Hiroshi @@aut@@ França Albuquerque, Pedro Lucas @@aut@@ Lewandowski, Tiago @@aut@@ Silva Oliveira Junior, Adair Da @@aut@@ Araújo Rozales, João Victor @@aut@@ Ferreira, Milena Wolff @@aut@@ de Queiroz, Eduardo Quirino Arguelho @@aut@@ Marino Almanza, José Carlos @@aut@@ Pistori, Hemerson @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	306313960
dewey-sort	3550
id	ELV003917827
language_de	englisch
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author	Garcia, Vanir
spellingShingle	Garcia, Vanir ddc 550 bkl 48.68 misc Fingerling counter misc Dataset misc Pisciculture misc Fingerlings misc Computer vision A new image dataset for the evaluation of automatic fingerlings counting
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topic_title	550 690 DE-600 48.68 bkl A new image dataset for the evaluation of automatic fingerlings counting Fingerling counter Dataset Pisciculture Fingerlings Computer vision
topic	ddc 550 bkl 48.68 misc Fingerling counter misc Dataset misc Pisciculture misc Fingerlings misc Computer vision
topic_unstemmed	ddc 550 bkl 48.68 misc Fingerling counter misc Dataset misc Pisciculture misc Fingerlings misc Computer vision
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title	A new image dataset for the evaluation of automatic fingerlings counting
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title_full	A new image dataset for the evaluation of automatic fingerlings counting
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author_browse	Garcia, Vanir Sant’Ana, Diego André Garcia Zanoni, Vanda Alice Brito Pache, Marcio Carneiro Naka, Marco Hiroshi França Albuquerque, Pedro Lucas Lewandowski, Tiago Silva Oliveira Junior, Adair Da Araújo Rozales, João Victor Ferreira, Milena Wolff de Queiroz, Eduardo Quirino Arguelho Marino Almanza, José Carlos Pistori, Hemerson
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title_sort	a new image dataset for the evaluation of automatic fingerlings counting
title_auth	A new image dataset for the evaluation of automatic fingerlings counting
abstract	Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers.
abstractGer	Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers.
abstract_unstemmed	Fingerling counting in fish farming is still a problem to be solved, although there are some technological solutions used in experimental cases and controlled environments. In this paper, a fingerlings counter is evaluated using a new image database with 448 videos of the Pintado Real® fingerlings species. A total of 21,402 combinations of 6 parameters of the fingerlings counter were tested. In the training phase, 314 videos were randomly selected, which represent 70% of the database. The remaining 30%, corresponding to 134 videos, were used for testing. Focusing on the parameters that best recognize and track the fingerlings of Pintado Real®, it was obtained Pearson Coefficient of 0.9803 and a quadratic average error of 2.65 when comparing the manual and automatic counting. The results obtained six parameters sets that achieved these metrics, reaching higher performance on the fingerlings counter from a new image database. The image database used in our research is available for researchers.
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title_short	A new image dataset for the evaluation of automatic fingerlings counting
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author2	Sant’Ana, Diego André Garcia Zanoni, Vanda Alice Brito Pache, Marcio Carneiro Naka, Marco Hiroshi França Albuquerque, Pedro Lucas Lewandowski, Tiago Silva Oliveira Junior, Adair Da Araújo Rozales, João Victor Ferreira, Milena Wolff de Queiroz, Eduardo Quirino Arguelho Marino Almanza, José Carlos Pistori, Hemerson
author2Str	Sant’Ana, Diego André Garcia Zanoni, Vanda Alice Brito Pache, Marcio Carneiro Naka, Marco Hiroshi França Albuquerque, Pedro Lucas Lewandowski, Tiago Silva Oliveira Junior, Adair Da Araújo Rozales, João Victor Ferreira, Milena Wolff de Queiroz, Eduardo Quirino Arguelho Marino Almanza, José Carlos Pistori, Hemerson
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up_date	2024-07-06T21:13:50.224Z
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A new image dataset for the evaluation of automatic fingerlings counting

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