A Convolutional-Transformer Network for Crack Segmentation with Boundary
Awareness
- URL: http://arxiv.org/abs/2302.11728v3
- Date: Sun, 12 Nov 2023 03:46:41 GMT
- Title: A Convolutional-Transformer Network for Crack Segmentation with Boundary
Awareness
- Authors: Huaqi Tao, Bingxi Liu, Jinqiang Cui and Hong Zhang
- Abstract summary: Cracks play a crucial role in assessing the safety and durability of manufactured buildings.
We propose a novel convolutional-transformer network based on encoder-decoder architecture to solve this challenge.
- Score: 5.98717173705421
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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