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The following bibliography contains all publications indexed in this database that are linked with this name as either author, editor or any other kind of contributor.

  1. Dang, Minh / Wang, Hanxiang / Nguyen, Tri-Hai / Tightiz, Lilia / Dinh Tien, Liem / Nguyen, Tan N. / Nguyen, Ngoc Phi (2023): CDD-TR: Automated concrete defect investigation using an improved deformable transformers. In: Journal of Building Engineering, v. 75 (September 2023).

    https://doi.org/10.1016/j.jobe.2023.106976

  2. Li, Yanfen / Wang, Hanxiang / Dang, L. Minh / Song, Hyoung‐Kyu / Moon, Hyeonjoon (2023): Attention‐guided multiscale neural network for defect detection in sewer pipelines. In: Computer-Aided Civil and Infrastructure Engineering, v. 38, n. 15 (April 2023).

    https://doi.org/10.1111/mice.12991

  3. Dang, L. Minh / Wang, Hanxiang / Li, Yanfen / Nguyen, Le Quan / Nguyen, Tan N. / Song, Hyoung-Kyu / Moon, Hyeonjoon (2023): Lightweight pixel-level semantic segmentation and analysis for sewer defects using deep learning. In: Construction and Building Materials, v. 371 (March 2023).

    https://doi.org/10.1016/j.conbuildmat.2023.130792

  4. Dang, L. Minh / Wang, Hanxiang / Li, Yanfen / Nguyen, Le Quan / Nguyen, Tan N. / Song, Hyoung-Kyu / Moon, Hyeonjoon (2022): Deep learning-based masonry crack segmentation and real-life crack length measurement. In: Construction and Building Materials, v. 359 (December 2022).

    https://doi.org/10.1016/j.conbuildmat.2022.129438

  5. Dang, L. Minh / Wang, Hanxiang / Li, Yanfen / Park, Yesul / Oh, Chanmi / Nguyen, Tan N. / Moon, Hyeonjoon (2022): Automatic tunnel lining crack evaluation and measurement using deep learning. In: Tunnelling and Underground Space Technology, v. 124 (June 2022).

    https://doi.org/10.1016/j.tust.2022.104472

  6. Dang, L. Minh / Wang, Hanxiang / Li, Yanfen / Nguyen, Tan N. / Moon, Hyeonjoon (2022): DefectTR: End-to-end defect detection for sewage networks using a transformer. In: Construction and Building Materials, v. 325 (March 2022).

    https://doi.org/10.1016/j.conbuildmat.2022.126584

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