Proposal of Innovative Methods for Computer Vision Techniques in Maritime Sector

Abstract

Computer vision (CV) techniques have been widely studied and applied in the shipping industry and maritime research. The existing literature has primarily focused on enhancing image recognition accuracy and precision for water surface targets by refining CV models themselves. This paper introduces innovative methods to further improve the accuracy of detection and recognition using CV models, including using ensemble learning and integrating shipping domain knowledge. Additionally, we present a novel application of CV techniques in the maritime domain, expanding the research perspective beyond the traditional focus on the accurate detection and recognition of water surface targets. Specifically, a novel solution integrating a CV model and the transfer learning method is proposed in this paper to address the challenge of relatively low-speed and high-charge internet services on ocean-going vessels, aiming to improve the online video viewing experience while conserving network resources. This paper is of importance for advancing further research and application of CV techniques in the shipping industry.

Keywords

• shipping industry
• maritime research
• computer vision
• ensemble learning
• transfer learning