EU-Net: a segmentation network based on semantic fusion and edge guidance for road crack images

Abstract

An enhanced U-shaped network (EU-Net) based on deep semantic information fusion and edge information guidance is studied to improve the segmentation accuracy of road cracks under hazy conditions. The EU-Net comprises multimode feature fusion, side information fusion and edge extraction modules. The feature and side information fusion modules are applied to fuse deep semantic information with multiscale features. The edge extraction module uses the Canny edge detection algorithm to guide and constrain crack edge information from the neural network. The experimental results show that the method in this work is superior to the most widely used crack segmentation methods. Compared with that of the baseline U-Net, the mIoU of the EU-Net increases by 0.59% and 5.7% on the Crack500 and Masonry datasets, respectively.