Journal of Applied Optics, Volume. 45, Issue 2, 373(2024)

Design and research on pavement crack segmentation based on convolutional neural network

Yanning LIU and Guobao ZHANG*
Author Affiliations
  • School of Automation, Southeast University, Nanjing 210000, China
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    References(31)

    [1] DONG Yuanshuai, ZHOU Xuli, HOU Yun et al. Optimization of the asphalt pavement pre-maintenance timing decision based on life cycle[J]. Highway, 65, 325-331(2020).

    [2] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

    [4] YANG X, LI H, YU Y et al. Automatic pixel-level crack detection and measurement using fully convolutional network[J]. Computer-Aided Civil and Infrastructure Engineering, 33, 1090-1109(2018).

    [5] CHOI W, CHA Y J. SDDNet: real-time crack segmentation[J]. IEEE Transactions on Industrial Electronics, 67, 8016-8025(2020).

    [6] YU Haiyang, JING Peng, ZHANG Wentao et al. Improved U-Net model for road crack detection by combining residuals and attention[J]. Computer Engineering, 49, 265-273(2023).

    [7] JI A, XUE X, WANG Y et al. An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement[J]. Automation in Construction, 114, 103176(2020).

    [10] LIN T Y, DOLLÁR P, GIRSHICK R et al. Feature pyramid networks for object detection[C], 2117-2125(2017).

    [11] LIU Z, MAO H, WU C Y et al. A convnet for the 2020s[C], 11976-11986(2022).

    [12] LIU Z, LIN Y, CAO Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2021).

    [13] HE K, ZHANG X, REN S et al. Deep residual learning for image recognition[C], 770-778(2016).

    [18] WANG P, CHEN P, YUAN Y et al. Understanding convolution for semantic segmentation[C], 1451-1460(2018).

    [20] ZHAO H, SHI J, QI X et al. Pyramid scene parsing network[C], 2881-2890(2017).

    [21] AMHAZ R, CHAMBON S, IDIER J et al. Automatic crack detection on 2D pavement images: an algorithm based on minimal path selection[J]. IEEE Transactions on Intelligent Transportation Systems, 17, 2718-2729(2015).

    [22] LIU Y, YAO J, LU X et al. DeepCrack: a deep hierarchical feature learning architecture for crack segmentation[J]. Neurocomputing, 338, 139-153(2019).

    [23] EISENBACH M, STRICKER R, SEICHTER D et al. How to get pavement distress detection ready for deep learning? A systematic approach[C], 2039-2047(2017).

    [25] YONG S, CUI L, QI Z et al. Automatic road crack detection using random structured forests[J]. IEEE Transactions on Intelligent Transportation Systems, 17, 3434-3445(2016).

    [26] ZOU Q, ZHANG Z, LI Q et al. DeepCrack: learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 28, 1498-1512(2019).

    [28] CHEN L C, PAPANDREOU G, KOKKINOS I et al. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2017).

    [29] XIE S, TU Z. Holistically-nested edge detection[C], 1395-1403(2015).

    [30] REN X X, XING Z C, XIA X et al. Neural network-based detection of self-admitted technical debt: from performance to explainability[J]. ACM Transactions on Software Engineering and Methodology, 28, 15.1-15.45(2019).

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    Yanning LIU, Guobao ZHANG. Design and research on pavement crack segmentation based on convolutional neural network[J]. Journal of Applied Optics, 2024, 45(2): 373

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    Paper Information

    Category: Research Articles

    Received: Apr. 23, 2023

    Accepted: --

    Published Online: May. 28, 2024

    The Author Email: Guobao ZHANG (章国宝(1965—))

    DOI:10.5768/JAO202445.0202004

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