A pavement crack synthesis method based on conditional generative adversarial networks

A Generative Adversarial Network (GAN) based asphalt pavement crack image generation method was proposed to improve the dataset size of the road images. Five open-source road crack datasets were leveraged to construct an image dataset, which contained two labels - transverse cracks and longitudinal cracks. The constructed dataset was used to facilitate crack detection and classification research by providing a diverse collection of labeled crack images derived from multiple public sources. The network structure of fully connected, convolutional and attention mechanisms based on the Conditional Generative Adversarial Network (CGAN) was used in this project. The purpose of this study was to train a generative model on selected categories of input pavement crack images and generate realistic crack images of those categories. We aim to tune the parameters of the GAN and optimize hyperparameters to improve the realism possibility of generated images. It also explored the generated images with different sizes and evaluated the performance of networks with different architectures. In particular, we analyzed the structural characteristics of conditional GAN. Results demonstrated that the Self-Attention Generative Adversarial Networks (SAGAN) model, which combines self-attention mechanisms with CGAN, can effectively address challenges related to limited crack image data and the inability to selectively generate images from specific categories. By conditioning the generator on category information, the SAGAN model was able to generate high-quality images while focusing on the target categories. Overall, the self-attention and conditional aspects of the SAGAN framework helped improve the generation of realistic pavement crack images.