# Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial Vehicles in Congested Harbors and Waterways

arXiv: https://arxiv.org/abs/2108.03862 · HTML: https://mlsanity.com/paper/2108.03862/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 12 of 12)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Training Deep Neural Networks on Noisy Labels with Bootstrapping](https://mlsanity.com/paper/1412.6596/) (2014)
- [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://mlsanity.com/paper/2101.06085/) (2021)
- [HarDNet: A Low Memory Traffic Network](https://mlsanity.com/paper/1909.00948/) (2019)
- [AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance](https://mlsanity.com/paper/2001.11737/) (2020)
- [Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance](https://mlsanity.com/paper/2104.06781/) (2021)
- [UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance](https://mlsanity.com/paper/2011.02853/) (2020)
- [End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.13599/) (2019)
- [On the Advantages of Multiple Stereo Vision Camera Designs for Autonomous Drone Navigation](https://mlsanity.com/paper/2105.12691/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Complete System for Automated 3D Semantic-Geometric Mapping of Corrosion in Industrial Environments](https://mlsanity.com/paper/2404.13691/) (2024)

## Abstract (arXiv)

In the maritime sector, safe vessel navigation is of great importance, particularly in congested harbors and waterways. The focus of this work is to estimate the distance between an object of interest and potential obstacles using a companion UAV. The proposed approach fuses GPS data with long-range aerial images. First, we employ semantic segmentation DNN for discriminating the vessel of interest, water, and potential solid objects using raw image data. The network is trained with both real and images generated and automatically labeled from a realistic AirSim simulation environment. Then, the distances between the extracted vessel and non-water obstacle blobs are computed using a novel GSD estimation algorithm. To the best of our knowledge, this work is the first attempt to detect and estimate distances to unknown objects from long-range visual data captured with conventional RGB cameras and auxiliary absolute positioning systems (e.g. GPS). The simulation results illustrate the accuracy and efficacy of the proposed method for visually aided navigation of vessels assisted by UAV.