Threshold Segmentation Method for Underwater Moving Luminous Targets Combined with Background Modeling

WANG Jia; GUAN Xia-wei; ZHANG Hao; FU Shao-bo; ZHANG Yu-ang; SONG Qing-hua

doi:10.11993/j.issn.2096-3920.2024-0107

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > Accepted Manuscript

WANG Jia, GUAN Xia-wei, ZHANG Hao, FU Shao-bo, ZHANG Yu-ang, SONG Qing-hua. Threshold Segmentation Method for Underwater Moving Luminous Targets Combined with Background Modeling[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0107

Citation:

WANG Jia, GUAN Xia-wei, ZHANG Hao, FU Shao-bo, ZHANG Yu-ang, SONG Qing-hua. Threshold Segmentation Method for Underwater Moving Luminous Targets Combined with Background Modeling[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0107

Citation:

WANG Jia, GUAN Xia-wei, ZHANG Hao, FU Shao-bo, ZHANG Yu-ang, SONG Qing-hua. Threshold Segmentation Method for Underwater Moving Luminous Targets Combined with Background Modeling[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0107

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Threshold Segmentation Method for Underwater Moving Luminous Targets Combined with Background Modeling

doi: 10.11993/j.issn.2096-3920.2024-0107

Wuhan Second Ship Design and Research Institute, Wuhan 430205, China

Received Date: 2024-06-05
Accepted Date: 2024-09-26
Rev Recd Date: 2024-09-17

Available Online: 2025-01-13

Abstract

Abstract

This paper presents an adaptive threshold segmentation algorithm combined with background modeling for the visual positioning of underwater moving luminous targets. Initially, background modeling is conducted based on the Gaussian Mixture Model to roughly screen out several regions with dynamic characteristics in the image. Subsequently, the features of the pixels within these regions are analyzed in the HSV color space to determine the presence of a target. After identifying regions containing the target, the Otsu method is applied to calculate the segmentation threshold within these regions. Finally, binarization processing is performed on the regions containing the target based on the calculated threshold to achieve target extraction. The algorithm is designed to alleviate the impact of brightness variations and clutter interference on visual positioning in complex underwater scenes. It fully utilizes the target’s motion state as well as its color and brightness attributes, combining background modeling with threshold segmentation to enhance the precision and stability of the segmentation process. Experimental results indicate that the algorithm demonstrates strong adaptability to common issues in underwater visual positioning, such as changes in brightness, halo blur, and bubble interference, and it is not dependent on the selection of initial parameters, making it suitable for engineering applications.
- moving luminous target,
- background modeling,
- threshold segmentation,
- HSV,
- Otsu method

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References(18)

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