Lightweight SAR Ship Detection via Contrastive Distillation
Deep convolutional and transformer-based detectors achieve strong performance for SAR ship detection but are often computationally prohibitive for real-time or onboard deployment. Lightweight models offer improved efficiency yet struggle to capture the complex structural relationships inherent in SAR backscatter. Most existing SAR knowledge-distillation approaches rely on feature or logit matching, which enforces localized activation similarity while neglecting the geometric relationships among object representations. We propose a Structured Unified Relational knowledGE distillation framework for SAR Ship detection (SURGE) that transfers relational geometry from a powerful teacher detecto...
| Authors | Surendar Devasundaram, Banafsheh Saber Latibari, Abhijit Mahalanobis |
|---|---|
| Primary category | cs.CV |
| Published | 27 May 2026 |
| Last updated at source | 1 June 2026 |
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