Abstract
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 spatiotemporal data fusion. The integration of AIS data with advanced computer vision techniques for target detection allows for real-time traffic analysis and provides detailed navigational dynamics of vessels. The method employs an enhanced Long Short-Term Memory network for precise trajectory prediction of AIS data and a single-stage target detection model for video data analysis. Experimental results demonstrate significant improvements in ship detection accuracy and tracking precision, with an average position prediction error of approximately 1.5 m, which outperforms existing methods. Additionally, a novel regional division and a Kalman filter-based method for AIS and video data fusion were proposed, effectively resolving the issues of data sparsity and coordinate transformation robustness under complex waterway conditions. This approach substantially advances the precision and efficiency of waterway monitoring systems, providing a robust theoretical and practical framework for the intelligent supervision of inland waterways.