Improved Deep Learning-based Sea-ice Monitoring System

This project will develop a robust software package that can be embedded with a camera system to provide an onboard sea-ice monitoring system. The software package consists of two main components: (1) Deep learning classification model, which involves a deep learning network trained to identify and classify sea-ice; (2) Lens artifact removal method, which is a set of morphological operations that remove any lens artifacts, which can be water droplets, particles, or objects on the camera lens obstructing the studied scene. This software package obtains the scene information in-situ, which is important for real-time sea-ice monitoring systems and enables the vessels to safely maneuver in such harsh icy water environments.

Faculty Supervisor:

Octavia Dobre

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Computer science

Sector:

Artificial Intelligence; Ocean Tech; Technology

University:

Memorial University of Newfoundland

Program: