1. A new image dataset for the evaluation of automatic fingerlings counting
- Author
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Eduardo Quirino Arguelho de Queiroz, Milena Wolff Ferreira, Pedro Lucas França Albuquerque, Adair da Silva Oliveira Junior, Vanir Garcia, Marco Hiroshi Naka, João Victor Araújo Rozales, Vanda Alice Garcia Zanoni, Hemerson Pistori, José Carlos Marino Almanza, Diego André Sant’Ana, Tiago Lewandowski, and Marcio Carneiro Brito Pache
- Subjects
0106 biological sciences ,Computer science ,business.industry ,010604 marine biology & hydrobiology ,Pattern recognition ,04 agricultural and veterinary sciences ,Aquatic Science ,01 natural sciences ,Pearson product-moment correlation coefficient ,Image (mathematics) ,symbols.namesake ,Image database ,040102 fisheries ,symbols ,0401 agriculture, forestry, and fisheries ,Training phase ,Artificial intelligence ,business - 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.
- Published
- 2020
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