An Investigation of Parameter Optimization in Fingerling Counting Problems
Resumo
The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination that obtained the best and worst results, in order to investigate the influence of such parameters in the performance of the software. From such results, it was concluded that the best combination reached 5.99 MAE and 9.96 RMSE.
Palavras-chave:
image/video analysis, automated counting, parameterization, computer vision
Referências
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M. F. Brabo, L. F. S. Pereira, J. V. M. Santana, D. A. V. Campelo, and G. C. Veras, “Cenário atual da produção de pescado no mundo, no brasil e no estado do pará: ênfase na aquicultura, acta of fisheries and aquatic resources,” Acta of Fisheries and Aquatic Resources, vol. 4, no. 2, pp. 50-58, 2016. [Online]. Available: https://www.tib.eu/de/suchen/id/awi%3Adoi%7E10.2312%252FActaFish.2016.4.2.50-58
P. A. Buckup, N. A. Menezes, and M. S. Ghazzi, Catálogo das espécies de peixes de água doce do Brasil. Museu Nacional Rio de Janeiro, 2007, vol. 1.
FAO, “Novo relatório da fao aponta que produção da pesca e aquicultura no brasil deve crescer mais de 100% até 2025,” 2016. [Online]. Available: http://www.fao.org/brasil/noticias/detail-events/pt/c/423722/
L. Sidonio, I. Cavalcanti, L. Capanema, R. Morch, G. Magalhães, J. Lima, V. Burns, A. J. Alves Júnior, and R. Mungioli, “Panorama da aquicultura no brasil: desafios e oportunidades,” BNDES setorial, vol. 35, pp. 421-463, 2012.
FAO, “PMA e OMS (2017) o estado da segurança alimentar e nutricional no mundo 2017: Construindo resiliência para a paz e segurança alimentar,” FAO.
L. Fan and Y. Liu, “Automate fry counting using computer vision and multi-class least squares support vector machine,” Aquaculture, vol. 380-383, pp. 91-98, 2013. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0044848612006175
Y. Duan, L. H. Stien, A. Thorsen, O. Karlsen, N. Sandlund, D. Li, Z. Fu, and S. Meier, “An automatic counting system for transparent pelagic fish eggs based on computer vision,” Aquacultural engineering, vol. 67, pp. 8-13, 2015.
C. Costa, M. Scardi, V. Vitalini, and S. Cataudella, “A dual camera system for counting and sizing northern bluefin tuna (thunnus thynnus; linnaeus, 1758) stock, during transfer to aquaculture cages, with a semi automatic artificial neural network tool,” Aquaculture, vol. 291, no. 3-4, pp. 161-167, 2009.
J. Hernández-Ontiveros, E. Inzunza-González, E. García-Guerrero, O. López-Bonilla, S. Infante-Prieto, J. R. Cárdenas-Valdez, and E. Tlelo-Cuautle, “Development and implementation of a fish counter by using an embedded system,” Computers and Electronics in Agriculture, vol. 145, pp. 53-62, 2018.
I. Klapp, O. Arad, L. Rosenfeld, A. Barki, B. Shaked, and B. Zion, “Ornamental fish counting by non-imaging optical system for real-time applications,” Computers and Electronics in Agriculture, vol. 153, pp. 126-133, 2018.
P. Albuquerque, V. Garcia, A. Oliveira, T. Lewandowski, C. Detweiler, A. Barbosa Goncalves, C. Costa, M. Naka, and H. Pistori, “Automatic live fingerlings counting using computer vision,” Computers and Electronics in Agriculture, vol. 167, p. 105015, 12 2019.
V. Garcia, D. A. Sant'Ana, V. A. Garcia Zanoni, M. C. Brito Pache, M. H. Naka, P. L. França Albuquerque, T. Lewandowski, A. D. Silva Oliveira Junior, J. V. Araújo Rozales, M. W. Ferreira, E. Q. A. de Queiroz, J. C. Marino Almanza, and H. Pistori, “A new image dataset for the evaluation of automatic fingerlings counting,” Aquacultural Engineering, vol. 89, p. 102064, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0144860919301700
Publicado
22/11/2021
Como Citar
OLIVEIRA JUNIOR, Adair da Silva et al.
An Investigation of Parameter Optimization in Fingerling Counting Problems. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 7-12.
DOI: https://doi.org/10.5753/wvc.2021.18881.
