Project Type: Neural Networks
Area/Sector: Marine Energy
Company/Partner: Nova Scotia Offshore Energy Research Association (OERA)/Bay of Fundy
Academic Institution: Dalhousie University
Lead Investigator: Dr. Sageev Oore
HQP: Dr. Scott Lowe
The aim of this project is to train a CNN model to detect and filter noise in the hydroacoustic sensor data, allowing OERA to improve the accuracy, consistency and manual effort required to pre-process its data.
By identifying “bad regions” in hydroacoustic data collected near underwater turbines in the Bay of Fundy with a model to automatically extract these regions from future survey data, OERA will be able to apply greater speed, consistency and accuracy to data processing to prepare for rigorous analysis. This project will produce an algorithm to reduce the pre-processing time for OERA hydroacoustic data automation. The overall system will be a visual analytics system with deep learning in the back end, trained on annotated images provided by OERA, and with the human expert annotator in the loop who reviews and corrects the automatically generated annotations. The human input is added to the training data improving the accuracy of the ML backend. The hypothesis is that the human-in-the-loop ML will significantly improve the speed of annotation.