Abstract:

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 proposes a transportation infrastructure crack image generation method based on Pix2pixHD. Firstly, the Pix2pixHD model is used to establish a spatial mapping relationship between real crack images and annotated labels based on a small amount of collected crack image data. Secondly, the objects in the label domain are 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 is transformed back to the image domain using the Pix2pixHD model, achieving adaptive augmentation of the transportation infrastructure crack dataset. Experimental results on the GAPS384, Tunnel200, and DeepCrack datasets demonstrate that the U-Net model trained with augmented data achieves higher detection accuracy and is more likely to avoid local optima. Compared to the DCGAN method, this approach exhibits better visual effects and FID quantification metric, thereby improving the generalization capability of the crack detection model in specific transportation infrastructure scenarios.

Key words: civil engineering, transportation infrastructure, image generation, crack detection, generative adversarial network