# Approximate Supervised Object Distance Estimation on Unmanned Surface Vehicles

arXiv: https://arxiv.org/abs/2501.05567 · HTML: https://mlsanity.com/paper/2501.05567/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Monocular Depth Estimation: A Survey](https://mlsanity.com/paper/1901.09402/) (2019)
- [LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark](https://mlsanity.com/paper/2308.09618/) (2023)
- [1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results](https://mlsanity.com/paper/2211.13508/) (2022)
- [Vision-Based Environmental Perception for Autonomous Driving](https://mlsanity.com/paper/2212.11453/) (2022)
- [The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024](https://mlsanity.com/paper/2311.14762/) (2023)
- [Stable Yaw Estimation of Boats from the Viewpoint of UAVs and USVs](https://mlsanity.com/paper/2306.14056/) (2023)
- [Fast Region of Interest Proposals on Maritime UAVs](https://mlsanity.com/paper/2301.11650/) (2023)

## Built on by (most cited first, 3 of 3)

- [3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results](https://mlsanity.com/paper/2501.10343/) (2025)
- [Real-Time Fusion of Visual and Chart Data for Enhanced Maritime Vision](https://mlsanity.com/paper/2507.13880/) (2025)
- [4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview](https://mlsanity.com/paper/2604.13244/) (2026)

## Abstract (arXiv)

Unmanned surface vehicles (USVs) and boats are increasingly important in maritime operations, yet their deployment is limited due to costly sensors and complexity. LiDAR, radar, and depth cameras are either costly, yield sparse point clouds or are noisy, and require extensive calibration. Here, we introduce a novel approach for approximate distance estimation in USVs using supervised object detection. We collected a dataset comprising images with manually annotated bounding boxes and corresponding distance measurements. Leveraging this data, we propose a specialized branch of an object detection model, not only to detect objects but also to predict their distances from the USV. This method offers a cost-efficient and intuitive alternative to conventional distance measurement techniques, aligning more closely with human estimation capabilities. We demonstrate its application in a marine assistance system that alerts operators to nearby objects such as boats, buoys, or other waterborne hazards.