Automated pixel-level surface crack detection using U-net

Abstract

Crack detection is significant for the inspection and diagnosis of concrete structures. Various automated approaches have been developed to replace human-conducted inspection, many of which are not adaptive to various conditions and unable to provide localization information. In this paper, an end-to-end semantic segmentation neural network based on U-net is employed to detect crack. Due to the limited number of available annotated samples, data augmentation is employed to avoid overfitting. The adopted network is trained by only 200 images of 512 × 512 pixels resolutions and achieves a satisfactory accuracy of 99.56% after 37 epochs. The output is an image of the same size as the input image where each pixel is assigned a class label, i.e. crack or not crack. It takes about 7s to process an image of designed size on CPU. Combined with sliding window technique, our model can cope with any image of larger size. Comparative experiment results show that our model outperforms traditional Canny and Sobel edge detection methods in a variety of complex environment without extracting features manually.