Generative Adversarial Networks for Road Crack Image Segmentation

Ziping Gao, Bo Peng, Tianrui Li, Cong Gou

Abstract: In this paper, we present a road crack segmentation method based on generative adversarial networks (GAN). Our GAN networks consist of two neural network models in terms of a generator and a discriminator, where two improved networks CU-Net and FU-Net are proposed based on U-Net. The U-Net, CU-Net and FU-Net are used as the generator, while two-class networks are used as the discriminator. The purpose of using the generator is to generate fake crack images which are very similar to real crack images. And the recognition is done by the discriminator to distinguish the real crack images from the fake crack images. After iterative training between the generator and the discriminator, the generator can generate fake crack images that are very similar to the real crack image. Finally, the generator can be used to segment the road crack images. Compared with the other state-of-the-art methods on three datasets, the proposed method achieves better performance. Specifically, the precision, recall and F1-score are 91.46%, 73.40%, and 77.33%, respectively on one of the public datasets.