arXiv Computer Vision By Yefan Wang, Xingyu Wang, Ruibiao Zhu, Yusen Wu

Hull First, Wake Second: Wake-Reliance Suppression for Robust Maritime Vessel Detection

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HullWake is a new maritime vessel detection framework that prioritizes hull detection before wake analysis to address the wake-reliance problem. It separates hull evidence from wake context, extracts wake cues via bidirectional proposal-anchored corridors, and suppresses wake-dominant predictions through multiple supervisory strategies. The method is evaluated on a wake-oriented dataset and demonstrates improvements in overall AP, robustness to weak or no-wake vessels, reduction of wake-like false positives, and stability of confidence after wake attenuation.

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arXiv AI
Sep 21

LoRA Enhanced Contrastive Learning with SAS Vision Transformers

The paper presents a three‑stage, parameter‑efficient approach to improve automatic target recognition (ATR) with synthetic aperture sonar (SAS) data by adapting DINOv3 Vision Transformers. Stage 1 applies Low‑Rank Adaptation (LoRA) while freezing the backbone, which significantly boosts the area under the precision‑recall curve from 0.300 to 0.679. Subsequent hard‑negative mining and supervised contrastive learning stages show negligible impact, indicating that a single LoRA adaptation is sufficient for effective underwater ATR.

By Dan Zimmerman, Frank E. Bobe III, Amelia L. McCormack, Matthew Cook, Gregory D. Vetaw