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    Optimized Pavement Crack Segmentation with Low Computational Cost Using Fusion-Enhanced Attention U-Net

    Source: Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 004::page 04026041-1
    Author:
    Mohammed, Mohammed Ameen
    ,
    Han, Zheng
    ,
    Li, Yange
    ,
    Al-Huda, Zaid
    ,
    Wang, Weidong
    DOI: 10.1061/JCCEE5.CPENG-6525
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAccurate segmentation of pavement cracks is crucial for maintaining road safety and the longevity of road infrastructures. Existing convolutional neural network (CNN) models often struggle with the noise and irregular crack patterns inherent in ...
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      Optimized Pavement Crack Segmentation with Low Computational Cost Using Fusion-Enhanced Attention U-Net

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4314394
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    contributor authorMohammed, Mohammed Ameen
    contributor authorHan, Zheng
    contributor authorLi, Yange
    contributor authorAl-Huda, Zaid
    contributor authorWang, Weidong
    date accessioned2026-08-20T21:24:11Z
    date available2026-08-20T21:24:11Z
    date copyright2026/04/29
    date issued2026
    identifier otherJCCEE5.CPENG-6525.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314394
    description abstractAbstractAccurate segmentation of pavement cracks is crucial for maintaining road safety and the longevity of road infrastructures. Existing convolutional neural network (CNN) models often struggle with the noise and irregular crack patterns inherent in ...
    publisherAmerican Society of Civil Engineers
    titleOptimized Pavement Crack Segmentation with Low Computational Cost Using Fusion-Enhanced Attention U-Net
    typeJournal Article
    journal volume40
    journal issue4
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/JCCEE5.CPENG-6525
    journal fristpage04026041-1
    journal lastpage04026041-14
    page14
    treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 004
    contenttypeFulltext
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