Crack Segmentation Based on Fusing Multi-Scale Wavelet and Spatial-Channel Attention

Abstract

Accurate and reliable crack segmentation is a challenge and meaningful task. In this article, aiming at the characteristics of cracks on the concrete images, the intensity frequency information of source images which is obtained by Discrete Wavelet Transform (DWT) is fed into deep learning-based networks to enhance the ability of network on crack segmentation. To well integrate frequency information into network an effective and novel DWTA module based on the DWT and scSE attention mechanism is proposed. The semantic information of cracks is enhanced and the irrelevant information is suppressed by DWTA module. And the gap between frequency information and convolution information from network is balanced by DWTA module which can well fuse wavelet information into image segmentation network. The Unet-DWTA is proposed to preserved the information of crack boundary and thin crack in intermediate feature maps by adding DWTA module in the encoder-decoder structures. In decoder, diverse level feature maps are fused to capture the information of crack boundary and the abstract semantic information which is beneficial to crack pixel classification. The proposed method is verified on three classic datasets including CrackDataset, CrackForest, and DeepCrack datasets. Compared with the other crack methods, the proposed Unet-DWTA shows better performance based on the evaluation of the subjective analysis and objective metrics about image semantic segmentation.