Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

Figure 2

Illustration of the crack detection network, CrackSeg. The multiscale dilated convolution module is used to capture abundant crack features. After fusing with the lower level crack features in the network, three 3 × 3 convolution operations are used continuously to improve the feature expression ability. The output feature of the last convolution layer is the crack feature maps, which is the input into the binary classifier for crack pixel-wise prediction.