Pavement crack detection and recognition using the architecture of segNet

This paper presents a practical deep-learning-based crack detection model for inspecting concrete pavement, asphalt pavement, and bridge deck cracks. Crack detection is a typical semantic segmentation task; thus, we propose an encoder-decoder structural model with a fully convolutional neural network, namely, PCSN, by referring to SegNet. This model accepts images of arbitrary size as input data and can be trained pixel by pixel. Moreover, VGG16 net is adopted without the top layer as the encoder, and it is initialized with open-source pretrained weights. "Adadelta" is employed as the optimizer and the cross-entropy is used as the loss function. a crack dataset of images containing complex crack textures is constructed by manual pixelwise annotation. Finally, the dataset is fed into PCSN to train and test the network. FCN-8s and MRCNN are also trained with the same dataset, and the experimental results demonstrate that the PCSN outperforms other algorithm on crack detection, additionally, the basic principle of methodological integration is also briefly introduced.