MambaDSF: Multi-Scale SSM with Dilated Feature Fusion for Sonar Small Target Detection
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 le...
| Authors | Hui Lin, Jiayi Li, Jing Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 May 2026 |
| Last updated at source | 7 October 2026 |
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