Multibeam imaging sonars can be used to monitor fish and marine mammal presence and behaviours in the near-field of tidal turbine installations, including evaluating avoidance, evasion, and potential blade strikes. Previous work in the Pathway Program recommended use of the Tritech Gemini 720is, which demonstrated a high level of utility for visually detecting and tracking targets from vessel and bottom-mounted orientations in tidal flows up to approximately 2.5 m/s in Grand Passage, Bay of Fundy, Nova Scotia.

This project focuses on a comparison of two approaches for automated analysis of Tritech Gemini 720is sonar data:

(1) an optical-based deep learning detection approach led by Dr. James Joslin, and

(2) an approach based on spatial and temporal filtering for target detection and tracking led by Dr. Benjamin Williamson.

The project scope was developed based on a practical need to increase efficiency in sonar data assessment, working toward methods that can incorporate reliable automation. The project goal was to advance the development of automated methods for detecting, tracking, and classifying acoustic targets in high energy tidal flows.

The results of this project will help inform the Department of Fisheries and Oceans Canada, tidal energy developers, and other stakeholders in the design and implementation of effective monitoring systems for tidal energy projects in the Bay of Fundy and beyond.