Robust visual perception of Aids to Navigation (AtoN) is essential for Maritime Autonomous Surface Ships (MASS) operating in restricted navigational waters, where estuarine clutter, fog, glare, and dense traffic can severely degrade detection reliability. Existing maritime vision datasets largely emphasize open-sea targets or coarse AtoN categories, leaving a granularity gap for IALA-compliant fine-grained understanding in riv...

The autonomous decision-making model for ship navigation requires extensive interaction and trial-and-error in real, complex environments to ensure optimal decision-making performance and efficiency across various scenarios. However, existing approaches still encounter significant challenges in addressing the temporal features of state space and tackling complex dynamic collision avoidance tasks, primarily due to factors such...

Increasing vessel traffic in narrow, winding inland waterways has heightened the risk of accidents, driving the need for improved surveillance and management. This study addresses the challenge of real-time processing and synchronization of voluminous video and AIS data for effective waterway management. We developed a surveillance method utilizing smart buoys equipped with sensors and edge computing devices, enabling dynamic...

The safety of ships during nighttime navigation has always been a major concern. With the widespread application of technologies such as intelligent recognition, intelligent detection, and unmanned ship navigation at night, nighttime maritime light pollution has significantly affected the effectiveness of these intelligent technologies and navigation safety. Therefore, effectively eliminating nighttime maritime light pollution...