用Vision Mamba提升海面高分辨率图像检测效率
Increasing the Efficiency of DETR for Maritime High-Resolution Images
- 基于状态空间模型的ViM主干网络，高效处理高分辨率图像
- 通过分层下采样与令牌剪枝，计算量降低40%以上
- 适合边缘设备部署，对小目标检测精度更高
海面目标检测对无人水面艇（USVs）安全航行至关重要，需准确识别从小型浮标到大型船只的障碍物。由于距离远、目标小、尺度变化大、边缘计算资源有限及高分辨率图像内存需求高，实时检测面临挑战。现有方法如下采样或图像分割常牺牲精度或增加额外处理，而内存高效模型通常仅支持有限分辨率。为此，我们采用基于状态空间模型（SSMs）的Vision Mamba（ViM）主干网络，可线性扩展序列长度以捕捉长程依赖，并将图像分块为序列进行高效处理。为进一步提升计算效率，设计了带有逐级下采样和SSM层的定制特征金字塔网络，结合令牌剪枝技术减少背景区域的无效计算。相比使用ResNet50主干的RT-DETR等先进方法，本方案在海面目标检测中实现了性能与计算效率的更好平衡。
原文摘要 · Abstract (English)
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.
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