Self-Supervised Adversarial Learning for Domain Adaptation of Pavement Distress Classification

Pavement distress classification is crucial for the maintenance of highways. Although many methods for classifying pavement distress are available, they all assume that training and testing datasets are drawn from the same distribution. When we introduce a new unlabeled dataset with a different distribution, the performance of existing methods decreases considerably due to domain shift, motivating us to look beyond the supervised setting to utilize unlabeled datasets directly in training a model. Therefore, we develop a novel unsupervised domain adaptation (UDA) framework, namely, the Self-supervised Adversarial Network (SSAN) for the first time in this study to conduct multi-category pavement distress classification on an unlabeled target domain. In particular, SSAN leverages adversarial domain adaptation (ADA) thoughts to align the features of different domains. However, distress typically occupies a small section of high-resolution pavement images. Consequently, aligning features directly is unreasonable because the aligning procedure is still dominated by background features instead of foreground features, which are the most useful information for classification. Therefore, we design a pretext module, called Self-supervised Learning for the Target domain (SLT), to mine foreground information. To validate our method, we use two challenging pavement crack datasets, namely, the Chonqing University Bituminous Pavement Disease Detection (CQU-BPDD) and the Chongqing University Bituminous Pavement Multi-label Disease Detection (CQU-BPMDD) datasets. Moreover, extensive experiments demonstrate that SSAN outperforms state-of-the-art UDA methods.