Integrating Shipping Domain Knowledge into Computer Vision Models for Maritime Transportation

Abstract

Maritime transportation plays a significant role in international trade and the global supply chain. To enhance maritime safety and reduce pollution to the marine environment, various regulations and conventions are proposed by international organizations. To ensure that shipping activities comply with the relevant regulations, more and more attention has been paid to maritime surveillance. Specifically, cameras have been widely equipped on the shore and drones to capture the videos of vessels. Then, computer vision (CV) methods are adopted to recognize the specific type of ships in the videos so as to identify illegal shipping activities. However, the complex marine environments may hinder the CV models from making accurate ship recognition. Therefore, this study proposes a novel approach of integrating the domain knowledge, such as the ship features and sailing speed, in CV for ship recognition of maritime transportation, which can better support maritime surveillance. We also give two specific examples to demonstrate the great potential of this method in future research on ship recognition.