Deep Learning based Segmentation of Fish in Noisy Forward Looking MBES Images
This addresses fish detection in marine environments for applications like fisheries monitoring, but it is incremental as it builds on existing CNN methods for semantic segmentation.
The paper tackles fish segmentation in noisy, low-resolution forward-looking multibeam echosounder images using a deep learning approach, achieving satisfying performance and generalization on a small dataset from the Danish Sound and Faroe Islands, with techniques for deployment on low-cost embedded platforms.
In this work, we investigate a Deep Learning (DL) approach to fish segmentation in a small dataset of noisy low-resolution images generated by a forward-looking multibeam echosounder (MBES). We build on recent advances in DL and Convolutional Neural Networks (CNNs) for semantic segmentation and demonstrate an end-to-end approach for a fish/non-fish probability prediction for all range-azimuth positions projected by an imaging sonar. We use self-collected datasets from the Danish Sound and the Faroe Islands to train and test our model and present techniques to obtain satisfying performance and generalization even with a low-volume dataset. We show that our model proves the desired performance and has learned to harness the importance of semantic context and take this into account to separate noise and non-targets from real targets. Furthermore, we present techniques to deploy models on low-cost embedded platforms to obtain higher performance fit for edge environments - where compute and power are restricted by size/cost - for testing and prototyping.