Sarr J. M. A., Brochier Timothée, Brehmer Patrice, Perrot Yannick, Bah A., Sarre A., Jeyid M. A., Sidibeh M., El Ayoubi S. (2021). Complex data labeling with deep learning methods : lessons from fisheries acoustics. Isa Transactions, 109, 113-125. ISSN 0019-0578.
Titre du document
Complex data labeling with deep learning methods : lessons from fisheries acoustics
Sarr J. M. A., Brochier Timothée, Brehmer Patrice, Perrot Yannick, Bah A., Sarre A., Jeyid M. A., Sidibeh M., El Ayoubi S.
Source
Isa Transactions, 2021,
109, 113-125 ISSN 0019-0578
Quantitative and qualitative analysis of acoustic backscattered signals from the seabed bottom to the sea surface is used worldwide for fish stocks assessment and marine ecosystem monitoring. Huge amounts of raw data are collected yet require tedious expert labeling. This paper focuses on a case study where the ground truth labels are non-obvious: echograms labeling, which is time-consuming and critical for the quality of fisheries and ecological analysis. We investigate how these tasks can benefit from supervised learning algorithms and demonstrate that convolutional neural networks trained with non-stationary datasets can be used to stress parts of a new dataset needing human expert correction. Further development of this approach paves the way toward a standardization of the labeling process in fisheries acoustics and is a good case study for non-obvious data labeling processes.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Ressources halieutiques [040]
;
Informatique [122]
