An Improved Nondestructive Semantic Segmentation Method for Concrete Dam Surface Crack Images with High Resolution

Abstract

To nondestructive semantic segment the crack pixels in the image with high resolution, previous methods often use sliding window and the crack patches to train the FCNs, and then use the trained FCNs for crack recognition. However, the FCNs will produce a higher proportion of false crack predictions with messy distributions in the high-resolution image. A CNN-to-FCN method is proposed to solve this problem. The CNN is trained by all the patches for large-scale crack and background recognition, and the screened crack predictions are then segmented by the FCN. A real-world concrete dam surface crack image database is firstly established to verify the improved method. The results indicated that (1) the improved method can extremely avoid the higher proportion of false crack predictions and their messy distributions in the high-resolution image through the full utilization of background patches and large-scale background recognition; (2) the ResNetv2 backbone and DeepLabv3 architecture recommended by the improved method can be further modified by reducing the bottleneck channels and adding a DUC module to achieve better performance; (3) the improved method can also reduce the prediction time when the image has low proportion of crack patches, which becomes more practicable for the engineering applications.