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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...
Detecting vessels engaging in illegal activities is of paramount importance for maritime security. One of the major goals is to detect dark vessels, ships that disable their transponders to evade surveillance.
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.
The paper introduces a pipeline that localizes frames from historical PTZ maritime video onto a reference panorama and uses context-aware sampling to build compact, scene‑specific training sets for ship detection. By enriching frames with weather and solar metadata and applying diversity sampling, the method reduces 40,718 candidate frames to just 220 for annotation, achieving a 99.5% reduction. Fine‑tuned YOLOv26‑m on this curated subset attains high detection performance (AP50 ≈ 94.8% and AP50‑95 ≈ 75.1%).
The paper introduces a pipeline that localizes frames from historical PTZ maritime video onto a reference panorama and then selects a context‑aware, diverse subset for ship detection training. By combining SuperPoint‑LightGlue localization, weather and solar‑state metadata, and diversity sampling, the method reduces 40,718 candidate frames to just 220 images for annotation. Fine‑tuned YOLOv26 on this compact set achieves high detection performance (AP50 ≈ 94.8%) while cutting annotation effort by 99.5%.