Optics and Precision Engineering, Volume. 32, Issue 2, 286(2024)

Concrete crack segmentation combined with linear guidance and mesh optimization

Guanghui LIU1,2、*, Jian CHEN1,2, Yuebo MENG1,2, and Shengjun XU1,3
Author Affiliations
  • 1College of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an70055,China
  • 2Higher Education Key Laboratory of Construction Robot in Shaanxi Province, Xi'an710055,China
  • 3Xi'an Key Laboratory of Intelligent Technology for Building and Manufacturing,Xi'an710055,China
  • show less
    References(28)

    [1] Q SONG, G Y LIN, J Q MA et al. An edge-detection method based on adaptive canny algorithm and iterative segmentation threshold, 64-67(2016).

    [2] Q Q LI, X L LIU. Novel approach to pavement image segmentation based on neighboring difference histogram method, 792-796(2008).

    [3] S G WU, Y K LIU. A segment algorithm for crack dection, 674-677(2012).

    [4] Y SHI, L M CUI, Z Q QI et al. Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, 17, 3434-3445(2016).

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

    [7] [7] 李海丰, 景攀, 韩红阳. 基于可变形卷积与特征融合的机场道面裂缝检测算法[J]. 南京航空航天大学学报, 2021, 53(6): 981-988. doi: 10.16356/j.1005-2615.2021.06.018LIH F, JINGP, HANH Y. Airport pavement crack detection algorithm based on deformable convolution and feature fusion[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2021, 53(6): 981-988.(in Chinese). doi: 10.16356/j.1005-2615.2021.06.018

    [8] [8] 徐胜军, 郝明, 孟月波, 等. 基于特征增强整体嵌套网络裂缝检测方法[J]. 激光与光电子学进展, 2022, 59(10): 1010003. doi: 10.3788/LOP202259.1010003XUS J, HAOM, MENGY B, et al. Crack detection method of holistically-nested network based on feature enhancement[J]. Laser & Optoelectronics Progress, 2022, 59(10): 1010003.(in Chinese). doi: 10.3788/LOP202259.1010003

    [9] Z S XU, H Y GUAN, J KANG et al. Pavement crack detection from CCD images with a locally enhanced transformer network. International Journal of Applied Earth Observation and Geoinformation, 110, 102825(2022).

    [10] W P JI, Y Z ZHANG, P F HUANG et al. A neural network with spatial attention for pixel-level crack detection on concrete bridges, 481-486(2022).

    [11] [11] 李良福, 王楠, 武彪, 等. 基于改进PSPNet的桥梁裂缝图像分割算法[J]. 激光与光电子学进展, 2021, 58(22): 2210001. doi: 10.3788/LOP202158.2210001LIL F, WANGN, WUB, et al. Segmentation algorithm of bridge crack image based on modified PSPNet[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2210001.(in Chinese). doi: 10.3788/LOP202158.2210001

    [12] [12] 祝一帆, 王海涛, 李可, 等. 一种高精度路面裂缝检测网络结构:Crack U-Net[J]. 计算机科学, 2022, 49(1): 204-211. doi: 10.11896/jsjkx.210100128ZHUY F, WANGH T, LIK, et al. Crack U-net: towards high quality pavement crack detection[J]. Computer Science, 2022, 49(1): 204-211.(in Chinese). doi: 10.11896/jsjkx.210100128

    [13] [13] 瞿中, 陈雯. 基于空洞卷积和多特征融合的混凝土路面裂缝检测[J]. 计算机科学, 2022, 49(3): 192-196. doi: 10.11896/jsjkx.210100164QUZ, CHENW. Concrete pavement crack detection based on dilated convolution and multi-features fusion[J]. Computer Science, 2022, 49(3): 192-196.(in Chinese). doi: 10.11896/jsjkx.210100164

    [14] Z QU, W CHEN, S Y WANG et al. A crack detection algorithm for concrete pavement based on attention mechanism and multi-features fusion. IEEE Transactions on Intelligent Transportation Systems, 23, 11710-11719(2022).

    [15] Z QU, C Y WANG, S Y WANG et al. A method of hierarchical feature fusion and connected attention architecture for pavement crack detection. IEEE Transactions on Intelligent Transportation Systems, 23, 16038-16047(2022).

    [16] Z QU, C CAO, L LIU et al. A deeply supervised convolutional neural network for pavement crack detection with multiscale feature fusion. IEEE Transactions on Neural Networks and Learning Systems, 33, 4890-4899(2022).

    [17] Q B HOU, L ZHANG, M M CHENG et al. Strip pooling: rethinking spatial pooling for scene parsing, 4002-4011(2020).

    [18] F YANG, L ZHANG, S J YU et al. Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Transactions on Intelligent Transportation Systems, 21, 1525-1535(2020).

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

    [20] Q ZHOU, Z QU, F R JU. A lightweight network for crack detection with split exchange convolution and multi-scale features fusion. IEEE Transactions on Intelligent Vehicles, 8, 2296-2306(2023).

    [21] C Q LIU, C G ZHU, X XIA et al. FFEDN: feature fusion encoder decoder network for crack detection. IEEE Transactions on Intelligent Transportation Systems, 23, 15546-15557(2022).

    [22] E SHELHAMER, J LONG, T DARRELL. Fully convolutional networks for semantic segmentation, 640-651(2017).

    [23] L C CHEN, G PAPANDREOU, I KOKKINOS et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

    [24] V BADRINARAYANAN, A KENDALL, R CIPOLLA. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

    [25] H S ZHAO, J P SHI, X J QI et al. Pyramid scene parsing network, 6230-6239(2017).

    [26] Q ZOU, Z ZHANG, Q Q LI et al. DeepCrack: learning hierarchical convolutional features for crack detection. IEEE Transactions on Image Processing, 1498-1512(2018).

    [28] O RONNEBERGER, P FISCHER, T BROX. U-net convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science, 234-241(2015).

    Tools

    Get Citation

    Copy Citation Text

    Guanghui LIU, Jian CHEN, Yuebo MENG, Shengjun XU. Concrete crack segmentation combined with linear guidance and mesh optimization[J]. Optics and Precision Engineering, 2024, 32(2): 286

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Jun. 2, 2023

    Accepted: --

    Published Online: Apr. 2, 2024

    The Author Email: LIU Guanghui (guanghuil@163.com)

    DOI:10.37188/OPE.20243202.0286

    Topics