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    UMDA: Lightweight and Efficient Crack Segmentation Model

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    JCCEE5.CPENG-6951.pdf (5.201Mb)
    Date
    2026
    Author
    Han, Beilin
    Zhang, Yihang
    Huang, Chuyue
    Ding, Wei
    Liu, Zhiwei
    Deng, Hongyang
    Wu, Jie
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    Abstract
    AbstractEfficient crack detection is vital for infrastructure safety, yet many deep learning models sacrifice practicality for precision, demanding resources beyond the reach of field-deployable devices. This paper presents UMDA (U-MobileNetV3-DECA-AUX), ...
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    http://yetl.yabesh.ir/yetl1/handle/yetl/4314458
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