Fish recognition in underwater environments using deep learning and audio data

Jean-François Laplante; Moulay A. Akhloufi; Cédric Gervaise

doi:10.1117/12.2585991

12 April 2021 Fish recognition in underwater environments using deep learning and audio data

Jean-François Laplante, Moulay A. Akhloufi, Cédric Gervaise

Proceedings Volume 11752, Ocean Sensing and Monitoring XIII; 117520G (2021) https://doi.org/10.1117/12.2585991
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Environmental conservation is an area where AI can provide significant help for many types of tasks. Oil, plastic, anthropogenic noise, overfishing and global warming are known to affect marine ecosystems (flora, fauna) inducing a drastic decrease of marine biodiversity and ecosystem services. The assessment of marine animals’ distribution could benefit from automatic recognition of the presence of a species in a specific location. For this purpose, the passive acoustics monitoring can use underwater audio recordings and try to recognize the sound produced by the species. This work compares the performance of classical computer vision algorithms and modern deep learning methods for the task of identifying if a spectrogram contains the characteristic sound produced by the brown meagre. An accuracy of 95% was achieved using a deep convolutional neural network based on a recent architecture and partially pretrained, outperforming classical computer vision algorithms.

Conference Presentation

Citation Download Citation

Jean-François Laplante, Moulay A. Akhloufi, and Cédric Gervaise "Fish recognition in underwater environments using deep learning and audio data", Proc. SPIE 11752, Ocean Sensing and Monitoring XIII, 117520G (12 April 2021); https://doi.org/10.1117/12.2585991

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