Research on sonar image generation technique based on improved CycleGAN

Tao Yu; Aiqing Chen; Xueling Chang; Yu Xie; Jie Liu

doi:10.1117/12.3029619

21 June 2024 Research on sonar image generation technique based on improved CycleGAN

Tao Yu, Aiqing Chen, Xueling Chang, Yu Xie, Jie Liu

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670I (2024) https://doi.org/10.1117/12.3029619
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Aiming at the problems of insufficient data volume and scarcity of target images in the target detection task of sonar images, this paper proposes an image style migration from optical to acoustic based on an improved cyclegan method based on the imaging principle and process of side-scan sonar images to realize the acoustic database augmentation so as to improve the status quo of the scarce number of samples of sonar images. In this paper, we enhance the feature extraction capability of the model network by introducing the cbam attention module to the original cyclegan generator, and evaluate the migration effect using the image quality assessment metrics is and fid. The experimental results conducted with the traditional method show that the method introducing the cbam attention mechanism module has a better style migration effect for sonar image generation.

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

Citation Download Citation

Tao Yu, Aiqing Chen, Xueling Chang, Yu Xie, and Jie Liu "Research on sonar image generation technique based on improved CycleGAN", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670I (21 June 2024); https://doi.org/10.1117/12.3029619

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KEYWORDS

Image processing

Education and training

Image quality

Data modeling

Gallium nitride

Image enhancement

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