FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation

Moseli Mots'oehli, Anton Nikolaev, Wawan B. IGede, John Lynham, Peter J. Mous, Peter Sadowski

Abstract:

Abstract—Fish stock assessment often involves manual fish
counting by taxonomy specialists, which is both time-consuming
and costly. We propose FishNet, an automated computer vision
system for both taxonomic classification and fish size estimation
from images captured with a low-cost digital camera. The system
first performs object detection and segmentation using a Mask
R-CNN to identify individual fish from images containing multiple
fish, possibly consisting of different species. Then each fish species
is classified and the length is predicted using separate machine
learning models. To develop the model, we use a dataset of 300,000
hand-labeled images containing 1.2M fish of 163 different species
and ranging in length from 10 cm to 250 cm, with additional
annotations and quality control methods used to curate highquality training data. On held-out test data sets, our system
achieves a 92% intersection over union on the fish segmentation
task, a 89% top-1 classification accuracy on single fish species
classification, and a 2.3 cm mean absolute error on the fish length
estimation task.