Automatic fish school classification for acoustic sensing of marine ecosystem

R. Lefort, Ronan Fablet, J. Boucher, L. Berger, S. Bourguignon
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Abstract

With the human demand for fish and the global warming effects, we know that marine populations are changing. Developing methods for observing and analyzing the spatio-temporal variations of marine ecosystems is then of primary importance. In this context, underwater acoustics remote sensing has a great potential. Operational systems mainly rely on expert interpretation of echograms acquired by sonar echosounders. In this works, we propose new algorithms for the analysis of acoustic survey regarding the inference of species mixing proportion. They rely on the definition and training of probabilistic school classification models from survey data.
用于海洋生态系统声传感的鱼群自动分类
随着人类对鱼类的需求和全球变暖的影响,我们知道海洋种群正在发生变化。因此,发展观测和分析海洋生态系统时空变化的方法至关重要。在此背景下,水声遥感具有很大的发展潜力。操作系统主要依赖于对声纳回声探测仪获得的回声图的专家解释。本文提出了基于物种混合比例推断的声学测量分析新算法。他们依赖于从调查数据中定义和训练概率学校分类模型。
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