Image generation of train wheel tread damage image based on improved generative adversarial network

Conghui Zhao

doi:10.1117/12.2636923

29 April 2022 Image generation of train wheel tread damage image based on improved generative adversarial network

Conghui Zhao

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 122470Q (2022) https://doi.org/10.1117/12.2636923
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

Aiming at the problem of insufficient data in the detection of train wheel tread damage, a tread image generation method based on generative adversarial network is proposed. In order to ensure the authenticity of the generated tread image, a symmetric skip connection network is used in the model to build a generative network, and the Wasserstein distance is introduced into the loss function of the network. This method effectively solves the problem of the shortage of tread damage image datasets, provides a data basis for the later detection of train wheel tread damage, and also provides technical support for the construction of train wheel image datasets.

Citation Download Citation

Conghui Zhao "Image generation of train wheel tread damage image based on improved generative adversarial network", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 122470Q (29 April 2022); https://doi.org/10.1117/12.2636923

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KEYWORDS

Gallium nitride

Image quality

Image processing

Convolution

Data modeling

Machine learning

Deconvolution

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