Crack image generation algorithm based on deep convolutional generative adversarial network

DingJun Zhang; MingChao Liao; XiXiang Wang; LaLao Gao

doi:10.1117/12.2667214

8 March 2023 Crack image generation algorithm based on deep convolutional generative adversarial network

DingJun Zhang, MingChao Liao, XiXiang Wang, LaLao Gao

Proceedings Volume 12586, Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022); 125860X (2023) https://doi.org/10.1117/12.2667214
Event: Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022), 2022, Xiangtan, China

Abstract

In order to improve the image quality of a specific class of crack images, as well as to solve the problems of insufficient size of the number of crack datasets and small number of complex crack images, a crack image generation model based on DCGAN (Deep Convolutional Generative Adversarial Network, DCGAN) is proposed, which has superior training stability and convergence speed. The experimental results show that DCGAN can generate a large number of real crack images with complex backgrounds more reliably than traditional image augmentation methods, effectively solving the problem of lack of crack images in special cases and greatly reducing the cost of crack image acquisition tasks.

Citation Download Citation

DingJun Zhang, MingChao Liao, XiXiang Wang, and LaLao Gao "Crack image generation algorithm based on deep convolutional generative adversarial network", Proc. SPIE 12586, Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022), 125860X (8 March 2023); https://doi.org/10.1117/12.2667214

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KEYWORDS

Education and training

Data modeling

Image quality

RGB color model

Roads

Convolution

Deep learning

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