# Image and AIS Data Fusion Technique for Maritime Computer Vision Applications

arXiv: https://arxiv.org/abs/2312.05270 · HTML: https://mlsanity.com/paper/2312.05270/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching](https://mlsanity.com/paper/2203.14148/) (2022)
- [Ship classification from overhead imagery using synthetic data and domain adaptation](https://mlsanity.com/paper/1905.03894/) (2019)

## Built on by (most cited first, 5 of 5)

- [Graph Learning-Driven Multi-Vessel Association: Fusing Multimodal Data for Maritime Intelligence](https://mlsanity.com/paper/2504.09197/) (2025)
- [Real-Time Fusion of Visual and Chart Data for Enhanced Maritime Vision](https://mlsanity.com/paper/2507.13880/) (2025)
- [Preventing Adversarial AI Attacks Against Autonomous Situational Awareness: A Maritime Case Study](https://mlsanity.com/paper/2505.21609/) (2025)
- [Fusing Monocular RGB Images with AIS Data to Create a 6D Pose Estimation Dataset for Marine Vessels](https://mlsanity.com/paper/2508.14767/) (2025)
- [Mix&Fix-Net: A Dual-Stage Trajectory Prediction Model for AIS and Vision-Derived Vessel Data](https://mlsanity.com/paper/2608.19580/) (2026)

## Abstract (arXiv)

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.