基于异构数据集的合成孔径雷达船只检测与总吨位估计以识别暗船
SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification
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中文总结 AI 辅助
研究利用异构数据集解决合成孔径雷达图像中船只检测及总吨位估计以识别暗船的问题。核心方法是结合多任务深度学习框架与非参数模型，通过特定K近邻算法进行总吨位回归。主要贡献是能预测多输出且在子任务上有竞争力，可识别暗船。
中文摘要 AI 辅助
检测从事非法活动的船只对海上安全至关重要，主要目标之一是检测暗船（关闭应答器以逃避监视的船只）。深度学习模型可在合成孔径雷达图像中检测船只，实现海上交通分析。但要检测潜在暗船，需基于总吨位选择必装应答器的船只。目前没有公开的合成孔径雷达数据集用于训练端到端的船只检测和总吨位回归深度学习模型。本文提出利用异构图像和表格数据集解决此任务的框架。该框架结合多任务深度学习框架预测船只位置、类型和物理尺寸，再级联非参数模型从船只大小和类别预测总吨位，通过使用混合欧几里得和分类距离测量样本相似度的K近邻算法进行总吨位回归。实验表明该解决方案能预测多个输出，在各个子任务上与现有模型竞争，从而实现暗船识别。
英文摘要
Detecting vessels engaging in illegal activities is of paramount importance for maritime security. One of the major goals is to detect dark vessels, ships that disable their transponders to evade surveillance. Deep Learning (DL) models can detect vessels in Synthetic Aperture Radar (SAR) images, enabling maritime traffic analysis regardless of weather or visibility conditions. However, to detect potential dark vessels, a DL model must select only those that are required to carry a transponder based on their Gross Tonnage (GT). Unfortunately, no public SAR dataset is available for training an end-to-end DL model for vessel detection and GT regression. In this work, we present a framework that leverages heterogeneous image and tabular datasets to solve this task. Our solution combines a multi-task DL framework for predicting the location, vessel type, and physical dimensions of ships, cascaded with a non-parametric model for predicting GT from vessel size and category. We perform GT regression by a KNN that measures sample similarity using a hybrid Euclidean and categorical distance. Experiments show that our solution can predict multiple outputs while remaining competitive with state-of-the-art models on individual subtasks, thus enabling the identification of dark vessels. We publish our code on GitHub https://github.com/PaltrinieriDavide/vesseldetection.
