Fusing Monocular RGB Images with AIS Data to Create a 6D Pose Estimation Dataset for Marine Vessels

The paper presents a novel technique for creating a 6D pose estimation dataset for marine vessels by fusing monocular RGB images with Automatic Identification System (AIS) data. The proposed technique addresses the limitations of relying purely on AIS for location information, caused by issues like equipment reliability, data manipulation, and transmission delays. By combining vessel detections from monocular RGB images, obtained using an object detection network (YOLOX-X), with AIS messages, the technique generates 3D bounding boxes that represent the vessels' 6D poses, i.e. spatial and rotational dimensions. The paper evaluates different object detection models to locate vessels in image space. We also compare two transformation methods (homography and Perspective-n-Point) for aligning AIS data with image coordinates. The results of our work demonstrate that the Perspective-n-Point (PnP) method achieves a significantly lower projection error compared to homography-based approaches used before, and the YOLOX-X model achieves a mean Average Precision (mAP) of 0.80 at an Intersection over Union (IoU) threshold of 0.5 for relevant vessel classes. We show indication that our approach allows the creation of a 6D pose estimation dataset without needing manual annotation. Additionally, we introduce the Boats on Nordelbe Kehrwieder (BONK-pose), a publicly available dataset comprising 3753 images with 3D bounding box annotations for pose estimation, created by our data fusion approach. This dataset can be used for training and evaluating 6D pose estimation networks. In addition we introduce a set of 1000 images with 2D bounding box annotations for ship detection from the same scene.

Image and AIS Data Fusion Technique for Maritime Computer Vision Applications

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.