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        TITLE = "Road damage detection with bounding box and generative adversarial
networks based augmentation methods",
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        TITLE = "Self-Supervised Adversarial Learning for Domain Adaptation of
Pavement Distress Classification",
        JOURNAL = ITS,
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        YEAR = "2024",
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        MONTH = "February",
        PAGES = "1966-1977",
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@article{bb225502,
        AUTHOR = "Li, K. and Yang, J. and Ma, S.W. and Wang, B. and Wang, S.S. and Tian, Y.J. and Qi, Z.Q.",
        TITLE = "Rethinking Lightweight Convolutional Neural Networks for Efficient
and High-Quality Pavement Crack Detection",
        JOURNAL = ITS,
        VOLUME = "25",
        YEAR = "2024",
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        MONTH = "January",
        PAGES = "237-250",
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@article{bb225503,
        AUTHOR = "Liu, Y.C. and Liu, F. and Liu, W. and Huang, Y.C.",
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Action Camera",
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        PAGES = "738-747",
        BIBSOURCE = "http://www.visionbib.com/bibliography/applicat845pave1.html#TT220499"}

@article{bb225504,
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        TITLE = "The Crack Diffusion Model: An Innovative Diffusion-Based Method for
Pavement Crack Detection",
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@inproceedings{bb225506,
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        TITLE = "A Method to Detect Pavement Surface Distress Based on Improved U-Net
Semantic Segmentation Network",
        BOOKTITLE = CVIDL23,
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@inproceedings{bb225508,
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        TITLE = "Automatic Detection for Road Voids from GPR Images using Deep
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@inproceedings{bb225509,
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@inproceedings{bb225510,
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@inproceedings{bb225512,
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@inproceedings{bb225514,
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@inproceedings{bb225515,
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Pavement Disease Segmentation",
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@inproceedings{bb225520,
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@inproceedings{bb225521,
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@inproceedings{bb225522,
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@inproceedings{bb225523,
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@inproceedings{bb225572,
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