Computer Vision-Based Deep Learning Algorithms for Detecting Marine Life and Physical Phenomena from Acoustic Backscatter Time Series

Large quantities of data are constantly acquired during underwater acoustic surveys for environmental monitoring and resources management. The data, visualized as 2D images, are typically analyzed manually or semi-automatically by experts (marine biologists, acousticians, oceanographers), which is time-consuming and prone to errors and inter-expert disagreements. The goal of the proposed research project is to develop new software tools for the automated processing and analysis of underwater acoustic data acquired with echosounders, using computer vision-based deep learning methods. We anticipate that this research, carried out in partnership with ASL Environmental Sciences Inc., will allow for the automatic detection of marine life, such as eulachon, sandlance, arctic cod, jellyfish, zooplankton, as well as various phenomena near the sea surface and sea bottom, such as air bubbles, waves, ice keels, and suspended sediments, from underwater acoustic data. The potential impacts are significant with respect to efforts in species abundance tracking and environmental monitoring, allowing for a switch from the traditional data analyses towards novel automatic methods reducing processing times, required man-power, and inconsistencies in the results.

Faculty Supervisor:

Alexandra Branzan Albu

Student:

Partner:

ASL Environmental Sciences Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Victoria

Program: