TY - CHAP
T1 - Using AI-Enhanced UAVs to Detect and Size Marine Contaminations
AU - Eldirdiry, Omer
AU - Nasiri, Navid
AU - Maashari, Ahmed Al
AU - Bourdoucen, Hadj
AU - Ghommam, Jawhar
AU - Saleem, Ashraf
AU - Rawas, Ghazi Al
AU - Al-Kamzari, Amran
AU - Ammari, Ahmed
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/2/12
Y1 - 2024/2/12
N2 - 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.
AB - 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.
KW - drones
KW - machine vision
KW - Marine contaminations
KW - remote sensing
KW - unmanned aerial vehicles
UR - http://www.scopus.com/inward/record.url?scp=85189629848&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85189629848&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c55d20cf-7e4e-375e-a2d7-9dc17bd0065f/
U2 - 10.1109/uvs59630.2024.10467155
DO - 10.1109/uvs59630.2024.10467155
M3 - Chapter
AN - SCOPUS:85189629848
SN - 9798350372557
T3 - 2024 2nd International Conference on Unmanned Vehicle Systems-Oman (UVS)
BT - 2nd International Conference on Unmanned Vehicle Systems-Oman, UVS 2024
A2 - Al-Hashim, Aliya
A2 - Pervez, Tasneem
A2 - Khriji, Lazhar
A2 - Waris, Muhammad Bilal
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Unmanned Vehicle Systems-Oman, UVS 2024
Y2 - 12 February 2024 through 14 February 2024
ER -