Augmentation Method of Transportation Infrastructure Crack Images

The crack detection model for transportation infrastructure based on deep learning relies on large-scale data for training. To address the problem of limited availability of diverse crack samples in specific transportation facility scenarios, this paper proposed a transportation infrastructure crack image augmentation method based on Pix2PixHD model. Initially, the Pix2PixHD model was used to establish a spatial mapping relationship between real crack images and annotated labels based on a small amount of collected crack image data. Subsequently, the objects in the label domain were edited to generate crack contours representing various forms, using methods such as label transfer from other datasets, manual editing, morphological dilation operations, and random superimposition operations. Finally, the edited label domain was transformed back to the image domain using the Pix2PixHD model, so as to achieve an adaptive augmentation of the transportation infrastructure crack dataset. This paper considered major materials and structures in transportation infrastructure (asphalt pavement, tunnel linings, and concrete structures) and conducted experiments using the GAPS384, Tunnel200, and DeepCrack datasets. Results demonstrate that the U-Net model trained on the augmented dataset achieves higher detection accuracy and is more likely to avoid local optima. Compared to the DCGAN model, the proposed method effectively controls crack morphology while maintaining the continuity of the crack skeleton, thereby enhancing the morphological diversity of the original crack dataset and improving the generalization capability of the detection model in specific transportation infrastructure scenarios.