A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness

• Published in: 2023 IEEE International Conference on Image Processing (ICIP) pp. 86 - 90
• Main Authors: Tao, Huaqi, Liu, Bingxi, Cui, Jinqiang, Zhang, Hong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08.10.2023
• Subjects: Architecture; Buildings; Convolutional codes; Convolutional Neural Network; Crack Segmentation; Deep Learning Application; Image segmentation; Safety; Task analysis; Transformers; Vision Transformer
• DOI: 10.1109/ICIP49359.2023.10222276

Cracks play a crucial role in assessing the safety and durability of manufactured buildings. However, the long and sharp topological features and complex background of cracks make the task of crack segmentation extremely challenging. In this paper, we propose a novel convolutional-transformer network based on encoder-decoder architecture to solve this challenge. Particularly, we designed a Dilated Residual Block (DRB) and a Boundary Awareness Module (BAM). The DRB pays attention to the local detail of cracks and adjusts the feature dimension for other blocks as needed. And the BAM learns the boundary features from the dilated crack label. Furthermore, the DRB is combined with a lightweight transformer that captures global information to serve as an effective encoder. Experimental results show that the proposed network performs better than state-of-the-art algorithms on two typical datasets. Datasets, code, and trained models are available for research at https://github.com/HqiTao/CT-crackseg.