论文标题
基于转移学习的海洋船只重新识别方法
A Transfer Learning-Based Approach to Marine Vessel Re-Identification
论文作者
论文摘要
海船重新识别技术是智能运输系统的重要组成部分，也是海洋监视所需的视觉感知任务的重要组成部分。但是，与陆地上的情况不同，海上环境是复杂且可变的，样品较少，并且在海上进行船舶重新识别更加困难。因此，本文提出了一种转移动态比对算法，并模拟了海上船只的摇摆状况，使用良好的且相似的军舰作为测试目标，以改善识别难度，从而应对复杂的海洋条件的影响，并讨论不同类型的船只作为转移对象的影响。实验结果表明，改进的算法将平均平均准确性（MAP）提高了10.2％，第一个命中率（RANK1）平均提高了4.9％。
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.
