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Hyeonjoon Moon

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. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. Hassan, Syed Ibrahim / Dang, L. Minh / Mehmood, Irfan / Im, Suhyeon / Choi, Changho / Kang, Jaemo / Park, Young-Soo / Moon, Hyeonjoon (2019): Underground sewer pipe condition assessment based on convolutional neural networks. In: Automation in Construction, v. 106 (October 2019).

    https://doi.org/10.1016/j.autcon.2019.102849

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