用神经网络提升海上威胁早期检测准确率，效果优于传统方法。
Evaluating Neural Networks for Early Maritime Threat Detection
- 用四种神经网络模型替代熵聚类，直接分类船只轨迹
- 完整轨迹下准确率达100%，随时间步减少平稳下降
- 适合需要高鲁棒性的海上安全监控场景
本文将船只轨迹分类作为评估海上威胁的代理任务。以往方法采用基于熵的度量将船行轨迹聚类为三类：随机游走、尾随和追逐。本文全面评估了神经网络方法在该任务上的表现，训练了四种神经网络模型，并与浅层学习方法在合成数据上进行对比。研究还考察了模型在不同时间步长下以及是否使用旋转增强数据时的准确性。为提升测试阶段鲁棒性，对轨迹进行归一化并采用旋转数据增强。结果表明，深度网络在完整轨迹上可达100%测试准确率，随时间步减少呈现平滑退化，显著优于基于熵的聚类方法。
原文摘要 · Abstract (English)
We consider the task of classifying trajectories of boat activities as a proxy for assessing maritime threats. Previous approaches have considered entropy-based metrics for clustering boat activity into three broad categories: random walk, following, and chasing. Here, we comprehensively assess the accuracy of neural network-based approaches as alternatives to entropy-based clustering. We train four neural network models and compare them to shallow learning using synthetic data. We also investigate the accuracy of models as time steps increase and with and without rotated data. To improve test-time robustness, we normalize trajectories and perform rotation-based data augmentation. Our results show that deep networks can achieve a test-set accuracy of up to 100% on a full trajectory, with graceful degradation as the number of time steps decreases, outperforming entropy-based clustering.
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