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Deep learning object detection methods, like YOLOv5, are effective inidentifying maritime vessels but often lack detailed information important forpractical applications. In this paper, we addressed this problem by developinga technique that fuses Automatic Identification System (AIS) data with vesselsdetected in images to create datasets. This fusion enriches ship images withvessel-related data, such as type, size, speed, and direction. Our approachassociates detected ships to their corresponding AIS messages by estimatingdistance and azimuth using a homography-based method suitable for both fixedand periodically panning cameras. This technique is useful for creatingdatasets for waterway traffic management, encounter detection, andsurveillance. We introduce a novel dataset comprising of images taken invarious weather conditions and their corresponding AIS messages. This datasetoffers a stable baseline for refining vessel detection algorithms andtrajectory prediction models. To assess our method's performance, we manuallyannotated a portion of this dataset. The results are showing an overallassociation accuracy of 74.76 %, with the association accuracy for fixedcameras reaching 85.06 %. This demonstrates the potential of our approach increating datasets for vessel detection, pose estimation and auto-labellingpipelines.

Image and AIS Data Fusion Technique for Maritime Computer Vision Applications