Instance Segmentation of Herring and Salmon Schools in Acoustic Echograms using a Hybrid U-Net

The automated classification of fish, such as herring and salmon, in multi-frequency echograms is important for ecosystems monitoring. This paper implements a novel approach to instance segmentation: a hybrid of deep-learning and heuristic methods. This approach implements semantic segmentation by a U-Net to detect fish, which are converted to instances of fish-schools derived from candidate components within a defined linking distance. In addition to four frequency channels of echogram data (67.5, 125, 200, 455 kHz), two simulated channels (water depth and solar elevation angle) are included to encode spatial and temporal information, which leads to substantial improvement in model performance. The model is shown to out-perform recent experiments that have used a Mask R-CNN architecture. This approach demonstrates the ability to classify sparsely distributed objects in a way that is not possible with state-of-the-art instance segmentation methods.