TY - JOUR
T1 - Automatic live fingerlings counting using computer vision
AU - França Albuquerque, Pedro Lucas
AU - Garcia, Vanir
AU - da Silva Oliveira, Adair
AU - Lewandowski, Tiago
AU - Detweiler, Carrick
AU - Gonçalves, Ariadne Barbosa
AU - Costa, Celso Soares
AU - Naka, Marco Hiroshi
AU - Pistori, Hemerson
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/12
Y1 - 2019/12
N2 - Fish counting is still a rudimentary process in most fisheries in Brazil. Current solutions are generally unaffordable for small and medium-size producers; hence, in order to provide a low-cost solution, this paper proposes a new technique for fish counting and presents a new image dataset to evaluate fish counting systems. The dataset is composed of a series of videos partially annotated at frame-level, which include approximately a thousand fish in high-resolution images. We describe a computer-vision based system that counts fish by combining information from blob detection, mixture of Gaussians and a Kalman filter. This work shows that the proposed method is a feasible approach for automatic fish counting, reducing costs and boosting production, as it increases labor availability. Our approach is efficient for fingerlings counting, with an average precision of 97.47%, recall of 97.61% and F-measure of 97.52% in the provided dataset.
AB - Fish counting is still a rudimentary process in most fisheries in Brazil. Current solutions are generally unaffordable for small and medium-size producers; hence, in order to provide a low-cost solution, this paper proposes a new technique for fish counting and presents a new image dataset to evaluate fish counting systems. The dataset is composed of a series of videos partially annotated at frame-level, which include approximately a thousand fish in high-resolution images. We describe a computer-vision based system that counts fish by combining information from blob detection, mixture of Gaussians and a Kalman filter. This work shows that the proposed method is a feasible approach for automatic fish counting, reducing costs and boosting production, as it increases labor availability. Our approach is efficient for fingerlings counting, with an average precision of 97.47%, recall of 97.61% and F-measure of 97.52% in the provided dataset.
KW - Aquaculture
KW - Computer vision
KW - Fish counting
KW - Fish farming
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U2 - 10.1016/j.compag.2019.105015
DO - 10.1016/j.compag.2019.105015
M3 - Article
AN - SCOPUS:85074279452
SN - 0168-1699
VL - 167
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 105015
ER -