2026-06-01原文2 min readUpdated: 2026-06-01

Lightweight SAR Ship Detection via Contrastive Distillation

The paper proposes SURGE, a knowledge distillation framework that transfers relational geometry from a teacher to a student detector using contrastive InfoNCE in a shared embedding space. It achieves up to 6.2 mAP and 8.0 AP75 gains on two-stage detectors, surpassing teacher performance on SSDD and HRSID benchmarks.

SourcearXiv Computer VisionAuthor: Surendar Devasundaram, Saber Latibari Banafsheh, Abhijit Mahalanobis

[2605.30380] Lightweight SAR Ship Detection via Contrastive Distillation

[Submitted on 27 May 2026]

Title:Lightweight SAR Ship Detection via Contrastive Distillation

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Abstract: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 detector to a compact student detector using a contrastive InfoNCE objective in a shared projection embedding space. To the best of our knowledge, this work presents the first transformer-based SAR ship detector knowledge distillation framework in SAR domain. The framework is architecture-agnostic in the sense that it provides a common region-level distillation interface for two-stage, one-stage and transformer-based detectors without modifying their deployed architectures. Experiments on the SSDD and HRSID benchmarks demonstrate that the proposed method yields substantial improvements for two-stage detectors, achieving up to 6.2 mAP and 8.0 AP75 gains over baseline student and even surpassing teacher performance

Comments: Accepted in GLSVLSI'26 special session 74: Efficiency In Computer Vision: From Image Generation to Decision"

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2605.30380 [cs.CV]

(or arXiv:2605.30380v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.30380

arXiv-issued DOI via DataCite

Submission history

From: Surendar Devasundaram [view email] [v1] Wed, 27 May 2026 21:57:28 UTC (186 KB)

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