  Deep learning object detection methods, like YOLOv5, are effective in
identifying maritime vessels but often lack detailed information important for
practical applications. In this paper, we addressed this problem by developing
a technique that fuses Automatic Identification System (AIS) data with vessels
detected in images to create datasets. This fusion enriches ship images with
vessel-related data, such as type, size, speed, and direction. Our approach
associates detected ships to their corresponding AIS messages by estimating
distance and azimuth using a homography-based method suitable for both fixed
and periodically panning cameras. This technique is useful for creating
datasets for waterway traffic management, encounter detection, and
surveillance. We introduce a novel dataset comprising of images taken in
various weather conditions and their corresponding AIS messages. This dataset
offers a stable baseline for refining vessel detection algorithms and
trajectory prediction models. To assess our method's performance, we manually
annotated a portion of this dataset. The results are showing an overall
association accuracy of 74.76 %, with the association accuracy for fixed
cameras reaching 85.06 %. This demonstrates the potential of our approach in
creating datasets for vessel detection, pose estimation and auto-labelling
pipelines.
