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
Fish stock assessment often involves manual fish counting by taxonomy
specialists, which is both time-consuming and costly. We propose an automated
computer vision system that performs both taxonomic classification and fish
size estimation from images taken 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 predicted
length using separate machine learning models. These models are trained on a
dataset of 50,000 hand-annotated images containing 163 different fish species,
ranging in length from 10cm to 250cm. Evaluated on held-out test data, 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 error on the fish length estimation task.