Computer vision-based detection of fish from acoustic backscatter time series

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Our proposed research addresses novel ways of detecting visual patterns from echosounder data using**computer vision techniques. Switching from the more traditional acoustic data processing paradigm to**computer vision algorithms may provide new insights into echosounder data, and may enable our partner**company to enrich their portfolio of automatic data processing services.**Our industrial partner is ASL Environmental Sciences Inc (ASL), an employee-owned company based in**Victoria, BC. ASL is a global leader in innovative solutions for environmental monitoring and has over 40**years of experience in oceanographic, acoustic, remote sensing and ice research products and scientific**consulting services. Since 2000, ASL has been building acoustic backscatter echosounders. To date, ASL has**provided very limited support for processing the data gathered from its acoustic backscatter echosounders.**Considering the 165 echosounder units that ASL has manufactured, the change in the clientele towards**non-acousticians, and the adoption of the technology for a greater number of applications, there is now an**opportunity to add new data processing services to ASL's portfolio. There are a number of potential application**areas that ASL is interested in exploring, such as automatic data processing for classification of suspended**sediments, frazil ice, ocean turbulence, oil in water, zooplankton/shrimp and fin fish.**This Engage collaboration will enable a focused short-term (six months) feasibility study addressing the**problem of fin fish detection from a computer vision perspective. We will exploit visual cues from echograms**to detect and classify fish with computer vision algorithms. Traditional segmentation methods, as well as recent**developments in deep learning will be explored.