Fisheries acoustics in Norway—Wide band data, autonomous platforms and deep learning

Abstract

This presentation gives an overview of recent achievements in fisheries acoustics associated with the Center for Research Based Innovation in Marine Acoustic Abundance Estimation and Backscatter Classification (CRIMAC), Norway. We present a data processing pipeline from raw acoustic data to survey indices of abundance for fisheries assessments as a test bench. This includes the raw data as well as manually worked up labels for acoustic target classification (ATC). We also collect data from dedicated CRIMAC surveys and organize broadband data across a range of species, using different echo sounder setting, to find optimal settings for various standard surveys. We have trained deep neural networks on labelled acoustic data for ATC and tested this in the pipeline. Semi-supervised methods are also developed, requiring only 10% of the training data compared to the supervised models. IMR has a developed a strategy for phasing in unmanned platforms in our surveys. The process is stepwise, where we first augment existing surveys to maintain time series integrity; this approach has been tested on our sand eel and sprat surveys. We also embed our deep learning models in containers to facilitate simple deployment. This allows adaptive survey strategies, which is a step towards fully autonomous acoustic surveys.