A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation

In recent years, convolutional neural-network-based crack segmentation methods have performed excellently. However, existing crack segmentation methods still suffer from background noise interference, such as dirt patches and pitting, as well as the imprecise segmentation of fine-grained spatial structures. This is mainly due to the fact that convolutional neural networks dilute low-level spatial information in the process of extracting deep semantic features, and the network cannot obtain accurate context awareness because of the limitation of the actual receptive field size. To address these problems, an encoder–decoder crack segmentation network based on multi-scale contextual information enhancement is proposed. First, a new architecture of skip connection is proposed, enabling the network to obtain refined crack segmentation results; then, a contextual feature enhancement module is designed to make the network more effective at distinguishing between cracks and background noise; finally, the deformable convolution is introduced into the encoder network to further enhance its ability to extract the diverse morphological features of cracks by adaptively adjusting the sampling area and the receptive field size. Experiments show that the proposed method is effective in crack segmentation and outperforms mainstream segmentation networks such as DeepLab V3+ and UNet++.