Publication details.


Author(s):M. Palmer, A. Álvarez-Ellacuría, V. Moltó, I.A. Catalán
Title:Automatic, operational, high-resolution monitoring of fish length and catch numbers from landings using deep learning
JCR Impact Factor:2.815
Abstract:Informed fishery management decisions require primary input data such as the fluctuations in the number of fish
landed and fish length. Obtaining these data can be costly if conducted by hand, which is the case for length data
in most fisheries. This cost often implies reduced sample sizes, which may introduce biases and lead to information
loss at, for example, the boat level. The recent boost in artificial intelligence applied to fisheries provides
a promising way to improve the assessment and management of stocks. We present an operational system using a
deep convolutional network (Mask R-CNN) coupled with a statistical model that automatically estimates the
number and the mean fork length of dolphinfish (Coryphaena hippurus) caught in a Mediterranean fishery with a
resolution of each landed fish box from each boat. The system operates on images of fish boxes collected
automatically at the centralized fish auction. The statistical model corrects for biases due to undetected fish using
the convolutional network and estimates the mean fork length of the fish in a box from the number of fish and the
box weight, allowing for high-resolution monitoring of fishery dynamics during the entire fishing season. The
system predictions were empirically validated and showed good accuracy and precision. Our system could be
readily incorporated into assessment schemes. We discuss how this type of monitoring system opens new opportunities
for improving fishery management.

Related staff

  • Ignacio A. Catalán Alemany
  • Miguel Palmer Vidal
  • Amaya Alvarez Ellacuria
  • Related departments

  • Marine Ecology
  • Related projects

  • RETORNO (CTA 137.2)