Hai-Wei WangRIH-TENG WU2025-01-212025-01-212025-0209265805https://www.scopus.com/record/display.uri?eid=2-s2.0-85213278330&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/724942Tile spalling poses significant threats to pedestrians on sidewalks. Recently, deep learning-based approaches have been developed for autonomous building assessments. However, training a supervised model typically requires a large labeled dataset, which is often unavailable in new domain tasks. Moreover, data acquisition and ground-truth labeling are costly. This paper presents an unsupervised framework for anomaly detection of tile spalling. The framework incorporates uncertainty estimation and contrastive learning by training a segmentation model on a source dataset containing known classes, excluding spalling. Spalling is subsequently identified as outlier pixels based on elevated uncertainty scores. Additionally, a synthetic pattern, dubbed “Spalling Craft”, is developed for outlier exposure to further enhance model performance. The proposed approach outperforms state-of-the-art baselines by approximately 18.4%, 46.6%, and 31.7% in AUC, AP, and FPR95 scores, respectively. Compared to supervised learning methods, the framework significantly improves data efficiency while achieving strong performance in tile spalling segmentation.trueFaçade anomaly detectionOutlier exposureStructural health monitoringUncertainty estimationUnsupervised deep contrastive learningjournal article10.1016/j.autcon.2024.1059412-s2.0-85213278330