Underwater optical beacon recognition based on dual image gray features and gaussian mixture model-triangle threshold algorithm

CHEN Zijun; YANG Ke; WANG Meng

doi:10.11993/j.issn.2096-3920.2026-0112

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Article Navigation > Journal of Unmanned Undersea Systems > 2026 > Accepted Manuscript

CHEN Zijun, YANG Ke, WANG Meng. Underwater optical beacon recognition based on dual image gray features and gaussian mixture model-triangle threshold algorithm[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0112

Citation:

CHEN Zijun, YANG Ke, WANG Meng. Underwater optical beacon recognition based on dual image gray features and gaussian mixture model-triangle threshold algorithm[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0112

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Underwater optical beacon recognition based on dual image gray features and gaussian mixture model-triangle threshold algorithm

doi: 10.11993/j.issn.2096-3920.2026-0112

CHEN Zijun^1
,,
YANG Ke^1
,,
WANG Meng^2
,

1.
School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
2.
School of Ocean and Civil Engineering, Shanghai JiaoTong University, Shanghai 200240, China

Received Date: 2026-06-12
Accepted Date: 2026-07-28
Rev Recd Date: 2026-07-11

Available Online: 2026-11-11

Abstract

Abstract

Underwater target recognition based on vision and optical beacon guidance is an important method to realize short-range underwater docking. The recognition accuracy of underwater optical beacons determines the positioning precision of underwater docking targets. A dual image gray feature-driven Gaussian Mixture Model (GMM)-Triangle Threshold Algorithm (TTA) is proposed to address the challenges of recognition accuracy and computational efficiency in underwater optical beacon segmentation. Firstly, taking dual image gray features as evaluation indices, the SVM is employed to determine whether an underwater image contains an optical beacon. Secondly, the GMM-TTA algorithm is designed to segment images to improve the recognition accuracy of underwater optical beacons. Finally, the proposed algorithm is verified using underwater images acquired from underwater docking experiments. The experimental results show that the proposed algorithm can accurately identify the existence of optical beacons in underwater images, effectively suppress strong underwater scattering halation during the image segmentation process, and the average roundness of extracted targets is superior to that of GMM, TTA, OTSU and GMM-OTSU algorithms.
- underwater optical beacon recognition,
- image segmentation,
- dual image gray features,
- Gaussian mixture model,
- triangle threshold algorithm

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References(19)

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