Maritime object detection is critical for the safe navigation of unmanned surface vessels (USVs), requiring accurate recognition of obstacles from small buoys to large vessels. Real-time detection is challenging due to long distances, small object sizes, large-scale variations, edge computing limitations, and the high memory demands of high-resolution imagery. Existing solutions, such as downsampling or image splitting, often reduce accuracy or require additional processing, while memory-efficient models typically handle only limited resolutions. To overcome these limitations, we leverage Vision Mamba (ViM) backbones, which build on State Space Models (SSMs) to capture long-range dependencies while scaling linearly with sequence length. Images are tokenized into sequences for efficient high-resolution processing. For further computational efficiency, we design a tailored Feature Pyramid Network with successive downsampling and SSM layers, as well as token pruning to reduce unnecessary computation on background regions. Compared to state-of-the-art methods like RT-DETR with ResNet50 backbone, our approach achieves a better balance between performance and computational efficiency in maritime object detection.

翻译：海洋目标检测对于无人水面艇（USV）的安全航行至关重要，需要从小型浮标到大型船舶等各种障碍物实现精确识别。由于远距离、小目标尺寸、大尺度变化、边缘计算限制以及高分辨率图像的高内存需求，实时检测颇具挑战性。现有解决方案（如下采样或图像分割）往往会降低精度或需要额外处理，而内存高效模型通常仅能处理有限分辨率。为克服这些限制，我们利用基于状态空间模型（SSM）的Vision Mamba（ViM）主干网络，该网络可在随序列长度线性扩展的同时捕捉长距离依赖关系。图像被标记化为序列，以实现高效的高分辨率处理。为进一步提升计算效率，我们设计了定制化的特征金字塔网络（Feature Pyramid Network），包含连续下采样和SSM层，以及用于减少背景区域不必要计算的令牌剪枝（token pruning）机制。与采用ResNet50主干网络的RT-DETR等最新方法相比，我们的方法在海洋目标检测中实现了性能与计算效率的更佳平衡。