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: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:2609.24872v1 Announce Type: new Abstract: Two-stage oriented detectors achieve high localization accuracy in synthetic aperture radar (SAR) ship detection, but their large backbones, feature py...
arXiv:2609.13647v1 Announce Type: new Abstract: The rapid development of unmanned aerial vehicle (UAV) technology has made aerial-image object detection increasingly important for natural-resource mo...
arXiv:2606. 01895v1 Announce Type: cross Abstract: With the growing number of satellites in low Earth orbit (LEO) constellations, the near-Earth space environment has become increasingly congested, making space object detection (SOD) a pressing challenge for space safety and sustainability.
arXiv:2608. 07018v1 Announce Type: cross Abstract: Horizon detection in images of ice-covered waters is a challenging problem for maritime navigation due to low contrast between water and sky, cluttered ice structures, and varying illumination conditions.
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. 09360v1 Announce Type: cross Abstract: The demand for maritime surveillance has given rise to the need for monitoring fishing vessel activities, particularly in addressing the challenge of "dark vessels" that operate without Automatic Identification System (AIS) transmission.
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...
UFO-DETR is an end‑to‑end object detection framework designed for UAV imagery, addressing challenges such as scale variation, dense distribution, and the prevalence of tiny targets. It employs an LSKNet backbone to optimize receptive fields and reduce parameters, integrates DAttention and AIFI modules for flexible multi‑scale spatial modeling, and introduces a DynFreq‑C3 module that enhances small target detection via cross‑space frequency feature enhancement. Experiments demonstrate that UFO‑DETR outperforms RT‑DETR‑L in detection accuracy while improving computational efficiency, making it suitable for UAV edge computing.
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.
arXiv:2608. 08025v1 Announce Type: cross Abstract: Accurate 3D reconstruction of ships at sea is important for maritime supervision, damage assessment, and autonomous maritime operations.
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.