Using AI-Enhanced UAVs to Detect and Size Marine Contaminations
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
This paper explores the utilization of unmanned aerial vehicles, remote sensing, and machine vision to detect and estimate the size of contaminations in seawater. The study outlines the essential setups and adjustments to simulate this process in indoor and outdoor settings. The proposed system is designed to chart the optimal path for a quadrotor, utilized in these experiments, allowing it to navigate and pinpoint oil spill locations within the test arena. The drone successfully detects and accurately reports the oil spill's location across multiple trials. The results confirm the effectiveness of the proposed system in detecting and assessing oil spills, showcasing its potential in real-world applications.
Publication Title
2nd International Conference on Unmanned Vehicle Systems-Oman, UVS 2024
ISBN
9798350372557
Recommended Citation
Eldirdiry, O.,
Nasiri, N.,
Maashari, A.,
Bourdoucen, H.,
Ghommam, J.,
Saleem, A.,
Rawas, G.,
Al-Kamzari, A.,
&
Ammari, A.
(2024).
Using AI-Enhanced UAVs to Detect and Size Marine Contaminations.
2nd International Conference on Unmanned Vehicle Systems-Oman, UVS 2024.
http://doi.org/10.1109/UVS59630.2024.10467155
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/713