Marine Robotics: An Improved Algorithm for Object Detection Underwater
Usman Ibrahim Musa1, Apash Roy2
1Usman Ibrahim Musa, School of Computer Applications, Lovely Professional University, Punjab, India.
2Apash Roy, School of Computer Applications, Lovely Professional University, Punjab, India.
Manuscript received on 16 August 2022 | Revised Manuscript received on 18 April 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 1-8 | Volume-2 Issue-2, August 2022 | Retrieval Number: 100.1/ijcgm.C72640911322 | DOI:10.54105/ijcgm.C7264.082222
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The visibility of items in water is lower than that of those on land. Light waves from a source don’t have enough time to reach an item before it vanishes beneath the surface because light waves in water travel more quickly than they do in air. As a result, it can be challenging for people to deal with water properly due to certain of its physical characteristics. In light of this, object detection underwater has a wide range of uses, including environmental monitoring, surveillance, search and rescue, and navigation. This might enhance the precision, efficiency, and safety of undersea activities. In light of the aforementioned, this paper presents an algorithm for detecting objects underwater using YOLOv5. The algorithm has been improved by changing the way YOLOv5 works, which makes it better at detecting small objects.We tested our algorithm and found that it is more accurate than the original YOLOv5 algorithm.
Keywords: Underwater Object Detection, Marine Robotics, Deep Learning, YOLOv5.
Scope of the Article: Robotics and Vision