MCL-CrackNet: A Concrete Crack Segmentation Network Using Multilevel Contrastive Learning

Pengfei Shi, Shen Shao, Xinnan Fan, Zhongkai Zhou, Yuanxue Xin

Abstract: Automatic concrete crack segmentation by computer vision is a challenging task due to the complex environments with uneven illumination, low resolution, and excessive noise. Meanwhile, existing methods suffer from low detection accuracy and weak generalization ability. To solve these problems, we propose a novel crack detection network using multilevel contrastive learning, called Multilevel Constrastive Learning-CrackNet (MCL-CrackNet). First, we leverage a Coordinate Convolution Block for extracting the features with geometric information by adding two channels with the coordinate information of the features. Thus, the extracted features with rich geometric information can have a better spatial perception. Then, we propose a position attention gate (PAG) module that fuses downsampling and upsampling features of the same size, and thus makes the fused features more focused on the position information of the cracks. Finally, we obtain more useful information of local and global features with multilevel contrastive learning instead of single-level contrastive learning. Compared with other previous methods, MCL-CrackNet achieves accurate detection with better generalization ability in both underwater dam cracks and pavement crack scenarios.

External IDs:dblp:journals/tim/ShiSFZX23