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Jingyi Liu

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Proceedings Article | 15 March 2019 Paper

Detection of sea surface obstacle based on super-pixel probabilistic graphical model and sea-sky-line

Liting Zhu, Jingyi Liu, Jinbo Chen

Proceedings Volume 11041, 110411X (2019) https://doi.org/10.1117/12.2522672

KEYWORDS: Image segmentation, Image processing algorithms and systems, Target detection, Image processing, Expectation maximization algorithms

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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.

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