Deep Domain Adaptation for Pavement Crack Detection
- URL: http://arxiv.org/abs/2111.10101v1
- Date: Fri, 19 Nov 2021 08:51:09 GMT
- Title: Deep Domain Adaptation for Pavement Crack Detection
- Authors: Huijun Liu, Chunhua Yang, Ao Li, Yongxin Ge, Sheng Huang, Xin Feng,
Zhimin Ruan
- Abstract summary: We propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN)
DDACDN learns to take advantage of the source domain knowledge to predict the multi-category crack location information in the target domain.
It outperforms state-of-the-art pavement crack detection methods in predicting the crack location on the target domain.
- Score: 9.937576059289269
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning-based pavement cracks detection methods often require
large-scale labels with detailed crack location information to learn accurate
predictions. In practice, however, crack locations are very difficult to be
manually annotated due to various visual patterns of pavement crack. In this
paper, we propose a Deep Domain Adaptation-based Crack Detection Network
(DDACDN), which learns to take advantage of the source domain knowledge to
predict the multi-category crack location information in the target domain,
where only image-level labels are available. Specifically, DDACDN first
extracts crack features from both the source and target domain by a two-branch
weights-shared backbone network. And in an effort to achieve the cross-domain
adaptation, an intermediate domain is constructed by aggregating the
three-scale features from the feature space of each domain to adapt the crack
features from the source domain to the target domain. Finally, the network
involves the knowledge of both domains and is trained to recognize and localize
pavement cracks. To facilitate accurate training and validation for domain
adaptation, we use two challenging pavement crack datasets CQU-BPDD and
RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement
Multi-label Disease Dataset named CQU-BPMDD, which contains 38994
high-resolution pavement disease images to further evaluate the robustness of
our model. Extensive experiments demonstrate that DDACDN outperforms
state-of-the-art pavement crack detection methods in predicting the crack
location on the target domain.
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