Deep Learning based Segmentation of Fish in Noisy Forward Looking MBES Images

TL;DR

This paper presents a deep learning approach for fish segmentation in noisy, low-resolution MBES images, demonstrating effective performance and deployment techniques on low-cost embedded platforms.

Contribution

The study introduces a CNN-based method for fish segmentation in noisy MBES images, with techniques for improved generalization and deployment on edge devices.

Findings

Achieved accurate fish/non-fish segmentation in noisy MBES images.

Demonstrated model deployment on low-cost embedded hardware.

Showed robustness with small, low-resolution datasets.

Abstract

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.