KSG-Net：面向海事三维船舶检测的关键稀疏与全局上下文学习网络
KSG-Net: Key-Sparse and Global-Context Learning for Maritime 3D Ship Detection
- School of Computer Science and Technology, Wuhan University of Science and Technology（武汉科技大学计算机科学与技术学院）
- State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人与智能系统国家重点实验室）
- China University of Chinese Academy of Sciences（中国科学院大学）
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology（武汉科技大学智能信息处理与实时工业系统湖北省重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对海事三维船舶检测的小型稀疏船舶特征弱、大型船舶全局结构建模不足的问题，本文提出KSG-Net，通过KSMA与GCA模块提升性能，在多尺度船舶检测中优于现有方法且鲁棒性强。
AI中文摘要：
海事环境中精确的三维船舶检测对自主导航至关重要，但由于船舶尺度差异大、小型船舶点云稀疏以及严重的海杂波干扰，该任务仍具挑战性。现有方法主要基于二维特征或密集表示，难以平衡检测精度与计算效率，而专为道路场景设计的稀疏三维检测器在海事场景中的泛化性能较差。本文聚焦于海事激光雷达感知中的两个关键挑战：小型稀疏船舶的特征表示薄弱，以及局部稀疏卷积感受野有限导致大型船舶的全局结构建模不足。为解决这些问题，我们提出KSG-Net，这是一种用于海事三维船舶检测的关键稀疏与全局上下文学习网络。核心思路是在统一的全稀疏检测框架内协同增强局部判别特征与全局结构感知能力。具体而言，设计了关键稀疏多尺度聚合（KSMA）模块，通过选择具有信息性的关键体素并聚合跨尺度邻域特征，增强小型稀疏船舶的表示；此外，引入全局上下文聚合（GCA）模块，通过带门控残差交互的场景级上下文建模捕捉长程几何依赖，从而提升大型船舶的表示。在泰晤士河船舶数据集和模拟数据集上的大量实验表明，KSG-Net在多尺度船舶检测中始终优于现有方法，且在复杂海事环境中表现出较强的鲁棒性。
英文摘要：
Accurate 3D ship detection in maritime environments is critical for autonomous navigation, yet remains challenging due to large-scale vessel variations, sparse point clouds of small vessels, and severe sea-clutter interference. Existing methods, primarily based on 2D features or dense representations, struggle to balance detection accuracy and computational efficiency, while sparse 3D detectors designed for road scenes generalize poorly to maritime scenarios. This paper focuses on two key challenges in maritime LiDAR perception: weak feature representation for small and sparse vessels, and insufficient global structural modeling for large vessels due to the limited receptive field of local sparse convolutions. To address these issues, we propose KSG-Net, a Key-Sparse and Global-Context learning network for maritime 3D ship detection. The core idea is to jointly enhance local discriminative features and global structural awareness within a unified fully sparse detection framework. Specifically, a Key Sparse Multi-scale Aggregation (KSMA) module is designed to enhance the representation of small and sparse vessels by selecting informative key voxels and aggregating cross-scale neighborhood features. Furthermore, a Global Context Aggregation (GCA) module is introduced to capture long-range geometric dependencies through scene-level context modeling with gated residual interactions, thereby improving the representation of large vessels. Extensive experiments on the Thames River vessel dataset and simulated datasets demonstrate that KSG-Net consistently outperforms existing methods in multi-scale vessel detection and exhibits strong robustness in complex maritime environments.
