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 - Funding Information:
This work has received financial support from the Dom Bosco Catholic University , Agropeixe Ltda (Projeto Pacu), the Foundation for the Support and Development of Education, Science and Technology from the State of Mato Grosso do Sul - FUNDECT (131/2016), the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil ( CAPES ) and the United States Department of Agriculture - National Institute of Food and Agriculture under grant USDA-NIFA 2017-67021-25924 . Additionally, some of the authors have been awarded with Scholarships from the Brazilian National Council of Technological and Scientific Development ( CNPq ).
Funding Information:
This work has received financial support from the Dom Bosco Catholic University, Agropeixe Ltda (Projeto Pacu), the Foundation for the Support and Development of Education, Science and Technology from the State of Mato Grosso do Sul - FUNDECT (131/2016), the Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - Brasil (CAPES) and the United States Department of Agriculture - National Institute of Food and Agriculture under grant USDA-NIFA 2017-67021-25924. Additionally, some of the authors have been awarded with Scholarships from the Brazilian National Council of Technological and Scientific Development (CNPq).
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 -