ICMAME 2023 Conference Proceedings
Paper No: 343
Paper Title: An Emulated Platform for Detecting and Size Measuring of Oil Spills Using An Unmanned Aerial Vehicle System
Paper Title: An Emulated Platform for Detecting and Size Measuring of Oil Spills Using An Unmanned Aerial Vehicle System
AUTHORS:
Omer Eldirdiry Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Ahmed Al Maashari Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Ashraf Saleem Department of Applied Computing, Michigan Technological University, Michigan, United States
Jawhar Ghommam Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Hadj Bourdoucen Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Navid Nasiri Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Ghazi Al Rawas Civil and Architectural Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Amran Al-Kamzari Pollution Operations Monitoring Centre, Environment Authority, Muscat, Sultanate of Oman
Ahmed Ammari Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
ABSTRACT:
This paper presents the machine vision techniques implemented in an emulated environment that mimics the detecting process of oil spills in the ocean. An image processing algorithm is developed to achieve accurate detection and size measurement for studying oil spill cases. This study demonstrates the required setups and adjustments performed to mimic this process on a smaller scale, in a lab-based experiment. The proposed emulated system generates the required path for the quadrotor, used in this experiment, to maneuver around the arena and detect the oil spill. The drone successfully detected and accurately provided the location of the oil spot for several trails. For these attempts, the areas of the detected oil were calculated and compared. Some discussions were stated regarding some findings and exceptional cases. In general, the results attest to the efficacy of the proposed system.
Keywords: Oil spill detection, Unmanned aerial vehicles, Machine vision, Remote sensing
Conference Venue: Mövenpick Hotel & Apartments Bur Dubai, Dubai-UAE
Conference Date: 29-30 April 2023
ISBN Number: 978-625-00-1526-1
DOI Number: https://doi.org/10.53375/icmame.2023.343
PDF Download
Omer Eldirdiry Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Ahmed Al Maashari Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Ashraf Saleem Department of Applied Computing, Michigan Technological University, Michigan, United States
Jawhar Ghommam Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Hadj Bourdoucen Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Navid Nasiri Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Ghazi Al Rawas Civil and Architectural Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
Amran Al-Kamzari Pollution Operations Monitoring Centre, Environment Authority, Muscat, Sultanate of Oman
Ahmed Ammari Electrical and Computer Engineering Department, Sultan Qaboos University, Muscat, Sultanate of Oman
ABSTRACT:
This paper presents the machine vision techniques implemented in an emulated environment that mimics the detecting process of oil spills in the ocean. An image processing algorithm is developed to achieve accurate detection and size measurement for studying oil spill cases. This study demonstrates the required setups and adjustments performed to mimic this process on a smaller scale, in a lab-based experiment. The proposed emulated system generates the required path for the quadrotor, used in this experiment, to maneuver around the arena and detect the oil spill. The drone successfully detected and accurately provided the location of the oil spot for several trails. For these attempts, the areas of the detected oil were calculated and compared. Some discussions were stated regarding some findings and exceptional cases. In general, the results attest to the efficacy of the proposed system.
Keywords: Oil spill detection, Unmanned aerial vehicles, Machine vision, Remote sensing
Conference Venue: Mövenpick Hotel & Apartments Bur Dubai, Dubai-UAE
Conference Date: 29-30 April 2023
ISBN Number: 978-625-00-1526-1
DOI Number: https://doi.org/10.53375/icmame.2023.343
PDF Download