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    Railway-Fastener Point Cloud Segmentation and Damage Quantification Based on Deep Learning and Synthetic Data Augmentation 

    Source: Journal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 002:;page 04024059-1
    Author(s): Weidong Wang; Haoran Niu; Shi Qiu; Jin Wang; Yangming Luo; Qasim Zaheer; Jun Peng
    Publisher: American Society of Civil Engineers
    Abstract: Accurate detection and quantification of damage to railway fasteners are crucial for ensuring railway safety. The spatial damage defects caused by the complex shape of fasteners and the problem of data imbalance in actual ...
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    DSpace software copyright © 2002-2015  DuraSpace
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