Detalls de la publicació.


Autor(s):A. Álvarez-Ellacuría, M. Palmer, I.A. Catalán, J.L. Lisani
Títol:Image-based, unsupervised estimation of fish size from commercial landings using deep learning
JCR Impact Factor:3.593
Resum:The dynamics of fish length distribution is a key input for understanding the fish population dynamics and taking informed management
decisions on exploited stocks. Nevertheless, in most fisheries, the length of landed fish is still made by hand. As a result, length estimation is
precise at fish level, but due to the inherent high costs of manual sampling, the sample size tends to be small. Accordingly, the precision of
population-level estimates is often suboptimal and prone to bias when properly stratified sampling programmes are not affordable. Recent
applications of artificial intelligence to fisheries science are opening a promising opportunity for the massive sampling of fish catches. Here,
we present the results obtained using a deep convolutional network (Mask R-CNN) for unsupervised (i.e. fully automatic) European hake
length estimation from images of fish boxes automatically collected at the auction centre. The estimated mean of fish lengths at the box level
is accurate; for average lengths ranging 20–40 cm, the root-mean-square deviation was 1.9 cm, and maximum deviation between the estimated
and the measured mean body length was 4.0 cm. We discuss the challenges and opportunities that arise with the use of this technology
to improve data acquisition in fisheries.

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