Maritime Surveillance by Multiple Data Fusion: An Application Based on Deep Learning Object Detection, AIS Data and Geofencing

Abstract

Marine traffic represents one of the critical points in coastal monitoring. This task has been eased by the development of Automatic Identification Systems (AIS), which allow ship recognition. However, AIS technology is not mandatory for all vessels, so there is a need for using alternative techniques to identify and track them. In this paper, we present the integration of several technologies. First, we perform ship detection by using different camera-based approaches, depending on the moment of the day (daytime or nighttime). From this detection, we estimate the vessel’s georeferenced position. Secondly, this estimation is combined with the information provided by AIS devices. We obtain a correspondence between the scene and the AIS data and we also detect ships without VHF transmitters. Together with a geofencing technique, we introduce a solution that fuses data from different sources, providing useful information for decision-making regarding the presence of vessels in near-shore locations.