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

WANG Jia; GUAN Xiawei; ZHANG Hao; FU Shaobo; ZHANG Yu'ang; SONG Qinghua

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

Volume 33 Issue 1

Mar 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(1): 92-98

WANG Jia, GUAN Xiawei, ZHANG Hao, FU Shaobo, ZHANG Yu'ang, SONG Qinghua. Threshold-Based Segmentation Method for Underwater Moving Luminous Targets Combined with Background Modeling[J]. Journal of Unmanned Undersea Systems, 2025, 33(1): 92-98. doi: 10.11993/j.issn.2096-3920.2024-0107

Citation:

WANG Jia, GUAN Xiawei, ZHANG Hao, FU Shaobo, ZHANG Yu'ang, SONG Qinghua. Threshold-Based Segmentation Method for Underwater Moving Luminous Targets Combined with Background Modeling[J]. Journal of Unmanned Undersea Systems, 2025, 33(1): 92-98. doi: 10.11993/j.issn.2096-3920.2024-0107

Citation:

WANG Jia, GUAN Xiawei, ZHANG Hao, FU Shaobo, ZHANG Yu'ang, SONG Qinghua. Threshold-Based Segmentation Method for Underwater Moving Luminous Targets Combined with Background Modeling[J]. Journal of Unmanned Undersea Systems, 2025, 33(1): 92-98. doi: 10.11993/j.issn.2096-3920.2024-0107

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Threshold-Based 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 presented an adaptive threshold-based segmentation method combined with background modeling for the visual positioning of underwater moving luminous targets. Initially, background modeling was conducted based on the Gaussian mixture model to screen out several dynamic regions in the image. Subsequently, the pixel features within these regions were analyzed in the HSV color space to determine the presence of a target. After identifying the target-containing regions, Otsu’s method was applied to calculate the threshold for segmentation within these regions. Finally, binarization was performed on target-containing regions based on the calculated threshold, facilitating target extraction. The algorithm was designed to alleviate the impact of brightness variations and clutter interference on visual positioning in complex underwater environments. It fully utilized the target’s motion state, as well as its color and brightness, combining background modeling with threshold-based segmentation to enhance the precision and stability of the segmentation. 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. Furthermore, it does not rely on the selection of initial parameters, making it suitable for engineering applications.
- underwater moving,
- luminous target,
- visual positioning,
- background modeling,
- threshold-based segmentation

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References(18)

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