Tsung-Wei HuangYi-Hsiang ChenJACOB JE-CHIAN LINCHUIN-SHAN CHEN2025-03-072025-03-072025-03https://www.scopus.com/record/display.uri?eid=2-s2.0-85214581320&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/725529On-site rebar inspection is crucial for structural safety but remains labor-intensive and time-consuming. While deep learning presents a promising solution, existing research often relies on limited real-world labeled data. This paper introduces a framework to train a deep learning model for on-site rebar instance segmentation without human labeling. Synthetic data are generated from BIM models, creating a Synthetic On-site Rebar Dataset (SORD) with 25,287 labeled images. Domain adaptation is incorporated to bridge the gap between synthetic and real-world non-labeled data. This approach eliminates the need for human labeling. It significantly enhances model performance, achieving a threefold improvement in Average Precision (AP) metrics compared to models trained on limited real-world data. Additionally, the proposed method demonstrates superior performance across various on-site rebar images collected online, underscoring its generalizability and practical applications.Building information modelingDeep learningDomain adaptationOn-site rebarSynthetic datajournal article10.1016/j.autcon.2024.105953