用稀疏融合结构提升声呐小目标检测精度，兼顾效率与泛化能力。
MambaDSF: Multi-Scale SSM with Dilated Feature Fusion for Sonar Small Target Detection
- 引入多尺度稀释特征融合机制，线性复杂度实现全局上下文建模。
- 在UATD数据集上达到91.5% mAP50，小目标子集提升2.2个百分点。
- 适合需要高精度小目标检测的水下感知系统开发者使用。
声呐成像是水下目标探测的主要模态，但小目标因像素覆盖不足、声学对比度低及成像距离下的尺度模糊而难以检测。基于CNN的检测器虽能高效提取局部特征，却缺乏全局声学上下文导致误报；基于Transformer的方法虽可捕捉长程依赖，但计算复杂度为二次方。现有基于Mamba的视觉模型虽具备线性扫描效率，但在金字塔层级间缺乏多尺度语义对齐、多感受野融合及小目标感知训练监督。本文提出MambaDSF，通过三项创新：增强型特征金字塔（MambaEFP）在低复杂度下联合捕获局部回波线索与全局声学上下文；稀释融合Mamba（DFMamba）强制跨层级多尺度特征对齐；以及尺度自适应加权IoU（SA-WIoU）与跨尺度一致性（CSC）损失，稳定小目标训练。MambaDSF在UATD前视声呐基准上取得91.5% mAP50，参数量仅2870万，优于所有对比方法。在小目标子集上提升2.2个百分点，跨域测试于FLS和MD-FLS验证了其泛化能力。代码已开源。
原文摘要 · Abstract (English)
Sonar imaging is the primary modality for underwater target detection, yet small targets remain difficult to detect due to insufficient pixel coverage, low acoustic contrast, and scale ambiguity across imaging ranges. CNN-based detectors extract local features efficiently but cannot suppress noise-induced false alarms without global acoustic context. Transformer-based methods capture long-range dependencies at quadratic computational cost. Existing Mamba-based vision models offer efficient linear-cost scanning but lack multi-scale semantic alignment across pyramid levels, multi-receptive-field fusion, and small-target-aware training supervision needed for reliable sonar detection. This letter proposes Mamba Dilated-Scale Fusion (MambaDSF), a hybrid framework addressing these limitations through three contributions: a Mamba Enhanced Feature Pyramid (MambaEFP) backbone that jointly captures local echo cues and global acoustic context at linear complexity; a Dilate Fusion Mamba (DFMamba) encoder that enforces multi-scale feature alignment across pyramid levels; and Scale-Adaptive Weighted IoU (SA-WIoU) and Cross-Scale Coherence (CSC) losses that stabilize small-target training. MambaDSF achieves 91.5% mAP50 on the UATD forward-looking sonar benchmark with 28.7 million parameters, surpassing all compared detectors. On a small-target subset the gain reached +2.2 percentage points, and cross-domain evaluation on FLS and MD-FLS confirms the generalization of the proposed architecture. The codes are publicly available at https://github.com/IDontKnowAAA/MambaDSF.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。