# Real-Time Fusion of Visual and Chart Data for Enhanced Maritime Vision

arXiv: https://arxiv.org/abs/2507.13880 · HTML: https://mlsanity.com/paper/2507.13880/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 44 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Efficient DETR: Improving End-to-End Object Detector with Dense Prior](https://mlsanity.com/paper/2104.01318/) (2021)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment](https://mlsanity.com/paper/2207.13085/) (2022)
- [SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water](https://mlsanity.com/paper/2105.01922/) (2021)
- [MODS -- A USV-oriented object detection and obstacle segmentation benchmark](https://mlsanity.com/paper/2105.02359/) (2021)
- [Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation](https://mlsanity.com/paper/1802.07956/) (2018)
- [Automatic Discovery and Geotagging of Objects from Street View Imagery](https://mlsanity.com/paper/1708.08417/) (2017)
- [1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results](https://mlsanity.com/paper/2211.13508/) (2022)
- [Image and AIS Data Fusion Technique for Maritime Computer Vision Applications](https://mlsanity.com/paper/2312.05270/) (2023)
- [Memory Maps for Video Object Detection and Tracking on UAVs](https://mlsanity.com/paper/2303.03508/) (2023)
- [Stable Yaw Estimation of Boats from the Viewpoint of UAVs and USVs](https://mlsanity.com/paper/2306.14056/) (2023)
- [3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results](https://mlsanity.com/paper/2501.10343/) (2025)
- [Approximate Supervised Object Distance Estimation on Unmanned Surface Vehicles](https://mlsanity.com/paper/2501.05567/) (2025)

## Built on by (most cited first, 2 of 2)

- [4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview](https://mlsanity.com/paper/2604.13244/) (2026)
- [Improved Vision-to-Chart Buoy Association with Learned World-to-Image Projection](https://mlsanity.com/paper/2605.22942/) (2026)

## Abstract (arXiv)

This paper presents a novel approach to enhancing marine vision by fusing real-time visual data with chart information. Our system overlays nautical chart data onto live video feeds by accurately matching detected navigational aids, such as buoys, with their corresponding representations in chart data. To achieve robust association, we introduce a transformer-based end-to-end neural network that predicts bounding boxes and confidence scores for buoy queries, enabling the direct matching of image-domain detections with world-space chart markers. The proposed method is compared against baseline approaches, including a ray-casting model that estimates buoy positions via camera projection and a YOLOv7-based network extended with a distance estimation module. Experimental results on a dataset of real-world maritime scenes demonstrate that our approach significantly improves object localization and association accuracy in dynamic and challenging environments.