Echofilter: A Deep Learning Segmentation Model Improves the Automation, Standardization, and Timeliness for Post-Processing Echosounder Data in Tidal Energy Streams
This improves automation and standardization for marine biologists and engineers analyzing fish abundance in turbulent tidal energy environments, though it is an incremental advance applying existing deep learning methods to a specific domain.
The paper tackled the problem of automating the segmentation of entrained-air boundaries in echosounder data from tidal energy streams, which is challenging due to turbulence. The result was Echofilter, a deep learning model that reduced average error to 0.33-1.0m, achieved 92-99% IoU with human annotations, and cut manual editing time by 50%.
Understanding the abundance and distribution of fish in tidal energy streams is important to assess risks presented by introducing tidal energy devices to the habitat. However tidal current flows suitable for tidal energy are often highly turbulent, complicating the interpretation of echosounder data. The portion of the water column contaminated by returns from entrained air must be excluded from data used for biological analyses. Application of a single conventional algorithm to identify the depth-of-penetration of entrained air is insufficient for a boundary that is discontinuous, depth-dynamic, porous, and varies with tidal flow speed. Using a case study at a tidal energy demonstration site in the Bay of Fundy, we describe the development and application of a deep machine learning model with a U-Net based architecture. Our model, Echofilter, was highly responsive to the dynamic range of turbulence conditions and sensitive to the fine-scale nuances in the boundary position, producing an entrained-air boundary line with an average error of 0.33m on mobile downfacing and 0.5-1.0m on stationary upfacing data, less than half that of existing algorithmic solutions. The model's overall annotations had a high level of agreement with the human segmentation, with an intersection-over-union score of 99% for mobile downfacing recordings and 92-95% for stationary upfacing recordings. This resulted in a 50% reduction in the time required for manual edits when compared to the time required to manually edit the line placement produced by the currently available algorithms. Because of the improved initial automated placement, the implementation of the models permits an increase in the standardization and repeatability of line placement.