FishNet: A Unified Embedding for Salmon Recognition
This addresses the need for non-invasive monitoring in aquaculture research, though it is incremental as it adapts an existing human identification method to a new domain.
The paper tackles the problem of identifying individual salmon for aquaculture by proposing FishNet, a deep learning architecture that achieves a false positive rate of 1% and a true positive rate of 96% using images of salmon heads.
Identifying individual salmon can be very beneficial for the aquaculture industry as it enables monitoring and analyzing fish behavior and welfare. For aquaculture researchers identifying individual salmon is imperative to their research. The current methods of individual salmon tagging and tracking rely on physical interaction with the fish. This process is inefficient and can cause physical harm and stress for the salmon. In this paper we propose FishNet, based on a deep learning technique that has been successfully used for identifying humans, to identify salmon.We create a dataset of labeled fish images and then test the performance of the FishNet architecture. Our experiments show that this architecture learns a useful representation based on images of salmon heads. Further, we show that good performance can be achieved with relatively small neural network models: FishNet achieves a false positive rate of 1\% and a true positive rate of 96\%.