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

• URL: http://arxiv.org/abs/2006.09034v1
• Date: Tue, 16 Jun 2020 09:57:38 GMT
• Title: Deep Learning based Segmentation of Fish in Noisy Forward Looking MBES Images
• Authors: Jesper Haahr Christensen, Lars Valdemar Mogensen, Ole Ravn
• Abstract summary: We build on recent advances in Deep Learning (DL) and Convolutional Neural Networks (CNNs) for semantic segmentation. We demonstrate an end-to-end approach for a fish/non-fish probability prediction for all range-azimuth positions projected by an imaging sonar. We show that our model proves the desired performance and has learned to harness the importance of semantic context.
• Score: 1.5469452301122177
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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