Automatic extraction method of bottom line based on expectation-maximization machine learning algorithm

Zhengxuan Xie; Gaixiao Li; Zikang Song; Ke Dai

doi:10.1117/12.3029074

22 May 2024 Automatic extraction method of bottom line based on expectation-maximization machine learning algorithm

Zhengxuan Xie, Gaixiao Li, Zikang Song, Ke Dai

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760U (2024) https://doi.org/10.1117/12.3029074
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

To address the challenge that the automatic extraction of bottom line is prone to interference from external factors, resulting in inaccurate detection and tracking of the bottom line in practical applications, this paper introduces the unsupervised learning expectation maximization algorithm (EM algorithm) into the peak detection and bottom line tracking stages of side-scan sonar images. An automatic extraction method of bottom line based on the EM machine learning algorithm is proposed. In order to evaluate the ability of the proposed method, experiments were conducted in simulated water environment. The results of the experiments show that the application of the EM machine learning method for automatic extraction of bottom line effectively addresses challenges such as occlusion by suspended particles and poor sea conditions. The experimental concludes that the proposed method achieves accurate detection and extraction of bottom line in complex environments, and exhibits promising practical applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhengxuan Xie, Gaixiao Li, Zikang Song, and Ke Dai "Automatic extraction method of bottom line based on expectation-maximization machine learning algorithm", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760U (22 May 2024); https://doi.org/10.1117/12.3029074

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