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.
Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most existing datasets predominantly focus on general o...
arXiv:2609.37003v1 Announce Type: new Abstract: Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most e...
arXiv:2608.29852v1 Announce Type: new Abstract: Automated maritime surveillance from satellite and aerial imagery requires large, precisely annotated datasets, which remain scarce for the instance-se...
arXiv:2607. 27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation.
arXiv:2607. 24453v1 Announce Type: cross Abstract: Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly.
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.
The paper introduces a new oriented ship detector that combines a C3k2_Strip module, which uses orthogonal strip convolutions to better capture elongated hull structures, with a Class-Aware Direction-Aware Exclusion Loss (CA-DAEL) that suppresses redundant predictions by leveraging class, direction, and confidence cues. Experiments on HRSC2016 and DIOR-R datasets show the method achieving 78.45% and 53.71% mAP50:95, respectively, with only 2.91M parameters. On HRSC2016, the approach outperforms the YOLOv11-OBB baseline by 6.32 percentage points in mAP50:95, highlighting its effectiveness for accurate oriented ship detection.
arXiv:2606. 15240v2 Announce Type: replace Abstract: Accurate vessel trajectory forecasting is essential for maritime situational awareness, navigation safety, traffic management, and autonomous navigation.
The paper introduces SASC-USOD, a framework for underwater salient object detection that learns spatially adaptive coordination between two structural representations: a boundary-sensitive representation using Laplacian filtering and a region-coherent representation via dual-range anisotropic large-kernel aggregation. A spatial coordination module estimates the relative reliability of these representations and adaptively blends them based on image content. Experiments on USOD10K and USOD benchmarks show that SASC-USOD outperforms existing methods, reducing MAE by 4.07% and 23.53% respectively, and its lightweight variant achieves 21 FPS on an NVIDIA Jetson TX2 NX.
The study investigates how the choice of annotation used to set a segmentation threshold influences retinal vessel segmentation performance. Using all 28 CHASE DB1 images and two human observers, the authors fit random forests and Extra Trees models, then compare five threshold policies—including fixed, observer‑tuned, mean‑observer, and maximin tuning—on the same score maps. Results show that maximin tuning alters thresholds in most fits but yields negligible changes in worst‑observer Dice scores, suggesting no accuracy advantage in this cohort.
KSG‑Net introduces a Key‑Sparse and Global‑Context learning framework for maritime 3D ship detection, addressing weak feature representation of small, sparse vessels and limited global modeling of large vessels. The network employs a Key Sparse Multi‑scale Aggregation module to select informative voxels and aggregate cross‑scale features, and a Global Context Aggregation module to capture long‑range geometric dependencies via scene‑level context modeling. Experiments on the Thames River vessel dataset and simulated data show that KSG‑Net outperforms existing methods in multi‑scale vessel detection and remains robust in complex maritime environments.
arXiv:2608.29626v1 Announce Type: new Abstract: Salient object detection in optical remote sensing images (ORSI-SOD) requires dense predictions that preserve object completeness and structural contin...