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Proceedings Paper

Detection of sea surface obstacle based on super-pixel probabilistic graphical model and sea-sky-line
Author(s): Liting Zhu; Jingyi Liu; Jinbo Chen
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Paper Abstract

With the development of marine resources, the USV (Unmanned Surface Vehicle) was widely used as a platform for autonomous navigation in the marine environment. In order to ensure the safe navigation of USV, this paper proposed a sea surface obstacle detection method based on probability graphical model and sea-sky-line. Our method utilized the SLIC algorithm to segment the sea surface image for image pre-processing. Then, we proposed the superpixel-based probability graphical model to segment the image, and the sea surface image would be divided into three main semantic regions and an obstacle region. Finally, we proposed a sea-sky-line detection algorithm. Based on this, obstacles within the sea-sky-line would be detected. The accuracy of this method has reached 82.1%, and the recall rate has reached 92.0%. The method can effectively avoid the interference of sea surface reflection and objects such as clouds in the sky, and has a good effect on the detection of obstacles.

Paper Details

Date Published: 15 March 2019
PDF: 10 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411X (15 March 2019); doi: 10.1117/12.2522672
Show Author Affiliations
Liting Zhu, Shanghai Univ. (China)
Jingyi Liu, Shanghai Univ. (China)
Jinbo Chen, Shanghai Univ. (China)

Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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