Hovering control of UUV through underwater object detection based on deep learning SCIE SCOPUS

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Title
Hovering control of UUV through underwater object detection based on deep learning
Author(s)
Jin, Han-Sol; Cho, Hyunjoon; Jiafeng, Huang; Lee, Jihyeong; Kim, Myung-Jun; Jeong, Sang Ki; Ji, Dae Hyeong; Joo, Kibum; Jung, Dongwook; Choi, Hyeung-Sik
KIOST Author(s)
Lee, Jihyeong(이지형)Jeong, Sang Ki(정상기)Ji, Dae Hyeong(지대형)
Alternative Author(s)
이지형; 정상기; 지대형
Publication Year
2022-06
Abstract
It is difficult to find a target object in water and hover an UUV at a relative position from the object to work on it. To solve this problem in this study, we used the geometric principles of camera image mapping to find the real-time position of an underwater object, based on which we investigated the hovering control of an UUV. For underwater object recognition, we used YOLOv2, a deep learning-based object detection algorithm, which has excellent real-time performance. To recognize objects in various underwater environments, the training was conducted on data obtained under different underwater environment conditions, such as illuminance, distance, and obstacles, and the performance of underwater object recognition was increased by using UUV to acquire training data in this study. An UUV was designed and fabricated to implement the proposed algorithm, and a hovering control algorithm was developed for the UUV to recognize a star-shaped object and control its relative distance and direction. The learning data were built, and the object recognition rate was increased using many real sea tests. Moreover, the camera mapping algorithm and the UUV control algorithm proposed in this paper were applied together and tested at sea, to achieve a stable control of the UUV.
ISSN
0029-8018
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/42473
DOI
10.1016/j.oceaneng.2022.111321
Bibliographic Citation
Ocean Engineering, v.253, 2022
Publisher
Pergamon Press Ltd.
Keywords
Underwater object detection; Hovering control; Unmanned underwater vehicle; YOLOv2; Deep learning
Type
Article
Language
English
Publisher
Pergamon Press Ltd.
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