A Transfer Learning-Based Approach to Marine Vessel Re-Identification

TL;DR

This paper introduces a transfer learning-based algorithm for marine vessel re-identification, addressing challenges posed by complex sea conditions and limited samples, with improved accuracy demonstrated through experiments.

Contribution

It proposes a novel transfer dynamic alignment algorithm tailored for maritime environments, enhancing vessel re-identification accuracy under challenging conditions.

Findings

mAP increased by 10.2%

Rank1 improved by 4.9%

effective in complex sea scenarios

Abstract

Marine vessel re-identification technology is an important component of intelligent shipping systems and an important part of the visual perception tasks required for marine surveillance. However, unlike the situation on land, the maritime environment is complex and variable with fewer samples, and it is more difficult to perform vessel re-identification at sea. Therefore, this paper proposes a transfer dynamic alignment algorithm and simulates the swaying situation of vessels at sea, using a well-camouflaged and similar warship as the test target to improve the recognition difficulty and thus cope with the impact caused by complex sea conditions, and discusses the effect of different types of vessels as transfer objects. The experimental results show that the improved algorithm improves the mean average accuracy (mAP) by 10.2% and the first hit rate (Rank1) by 4.9% on average.