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Performance Evaluation of Deep Learning Image Classification Modules in the MUN-ABSAI Ice Risk Management

Ravindu G Thalagala1, Oscar De Silva1, Dan Oldford2

  • 1Faculty of Engineering and Applied Science, Memorial University of Newfoundland (MUN), St. John's, NL A1B 3X5, Canada.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
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Arctic sea ice retreat opens new shipping routes, but poses navigational risks. A deep learning system using YOLOv8n-cls effectively classifies ice conditions, enabling safer Arctic maritime operations.

Area of Science:

  • Marine Navigation
  • Artificial Intelligence
  • Climate Change Impacts

Background:

  • Arctic sea ice decline is creating new shipping lanes.
  • These routes present significant navigational hazards due to ice conditions.
  • Advanced risk management systems are crucial for safe Arctic maritime operations.

Purpose of the Study:

  • To propose a deep learning-based Arctic ice risk management architecture.
  • To develop and evaluate modules for ice classification, risk assessment, ice floe tracking, and ice load calculations.
  • To assess the efficiency of the YOLOv8n-cls model for onboard ice classification.

Main Methods:

  • Developed a deep learning architecture with specialized modules for ice risk management.
  • Created a dataset of 15,000 ice images from public sources and the Canadian Coast Guard.
Keywords:
YOLOv8deep learningice classificationsea ice imagessea ice risk mitigation

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  • Evaluated the YOLOv8n-cls model for ice classification, training and testing across Roboflow, Google Colab, and Compute Canada platforms.

    • Main Results:

    • Image Classification Module I achieved 99.4% validation accuracy; Module II achieved 98.6%.
    • Inference times were under 1 second on Google Colab and under 3 seconds on a stand-alone system.
    • The system demonstrated high efficiency for real-time ice condition monitoring.

    Conclusions:

    • The proposed deep learning architecture effectively manages Arctic ice risks.
    • The YOLOv8n-cls model is suitable for resource-constrained onboard systems in the Arctic.
    • The system enhances the safety and efficiency of maritime navigation in Arctic waters.