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Overview: Cracks are one of the most common categories of pavement distress. Early locating and repairing the cracks can not only reduce the cost of pavement maintenance but also decrease the probability of road accidents happening. Precise measurement of the crack is an essential step toward identifying the road condition and determining rehabilitation strategies. Nevertheless, cracks do not have a certain shape and the appearance of cracks usually changes drastically in different lighting conditions, making it hard to be detected by the algorithm with imagery analytics. Therefore, fully automated and comprehensive crack detection is still challenging. In this paper, we focus on pixel-level crack detection in 2D vision and propose an effective U-shaped fully convolutional neural network called UCrackNet, which is the enhanced version of U-Net. It consists of three main components: an encoder, a bridge block, and a decoder. The backbone of the encoder is the pre-trained VGG-16 that extracts spatial features from the pavement image. The last convolutional layer at each scale in the encoder has a skip connection to connect the corresponding layer in the decoder to preserve and reuse feature maps at different pooling stages. To minimize the possibility of overfitting and achieve better generalization ability, we add a dropout layer into each skip connection. The bridge block is a bridge path between the encoder and the decoder. Motivated by DenseASPP, four densely connected atrous convolutional layers with different dilation rates are employed in the bridge block, so that it generates features with a larger receptive field to effectively capture multi-scale information. The decoder has four convolutional blocks, and in each block, up-sampling with indices is used to reduce the shift and distortion during the up-sampling operation. Furthermore, multi-level fusion is introduced in the output stage to utilize multiscale and multilevel information of objects. The idea of multi-level fusion is inspired by the success of the HED network architecture, which showed that it is capable of fully exploiting the rich feature hierarchies from convolutional neural network (CNNs). Specifically, the feature maps of each stage are first up-sampled to the size of the output image, then a 1×1 convolutional is used to fuse these maps to get the final prediction map. Qualitative evaluations on the two public CrackTree206 and AIMCrack datasets demonstrate that the proposed method achieves superior performance compared with CrackForest, LinkNet, ResUNet, U-Net, and DeepCrack. The qualitative results show that our method produces high-quality crack maps, which are closer to the ground-truth and have lower noise compared with the other methods.
The structure of the proposed UCrackNet
An illustration of pooling indices. The left and right sides show the operation of max-pooling and up-sampling, respectively
The bridge block with atrous convolution
The output stage using multi-level fusion
The precision-recall (PR) curves of various methods on the two datasets. (a) CrackTree206; (b) AIMCrack
The predicted results by different methods on the CrackTree206 dataset
The predicted results by different methods on the AIMCrack dataset
The predicted results by different methods on the challenging scenario