Unsupervised domain adaptation for crack detection

Abstract

The reliable and fast detection of cracks is crucial for assessing the condition and maintaining civil infrastructure. However, due to diverse construction materials, imaging conditions, and environmental interference, there exists a domain shift between crack images collected from civil infrastructure. This shift results in significant performance drops of crack detection models trained on one dataset when applied to another, limiting their cross-dataset applicability. To address this issue, this paper proposes DACrack, an unsupervised domain adaptation framework for crack detection of civil infrastructure. The proposed method performs domain adaptation at the input, feature, and output levels using contrastive mechanisms, adversarial learning, and variational autoencoders. Extensive experiments demonstrate the effectiveness and robustness of the proposed method for cross-dataset crack detection. By mitigating the impact of domain shift, DACrack offers a more reliable and accurate solution for assessing the condition of civil infrastructure.