• Laser & Optoelectronics Progress
  • Vol. 58, Issue 22, 2210001 (2021)
Liangfu Li, Nan Wang*, Biao Wu, and Xi Zhang
Author Affiliations
  • School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi 710119, China
  • show less
    DOI: 10.3788/LOP202158.2210001 Cite this Article Set citation alerts
    Liangfu Li, Nan Wang, Biao Wu, Xi Zhang. Segmentation Algorithm of Bridge Crack Image Based on Modified PSPNet[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2210001 Copy Citation Text show less
    References

    [1] Zhou X H, Zhang X G. Thoughts on the development of bridge technology in China[J]. Engineering, 5, 1120-1130, 1245-1256(2019).

    [2] Li W J, Zhang M, Shen Z H et al. Track crack detection method in complex environment[C]. //2018 11th International Symposium on Computational Intelligence and Design (ISCID), December 8-9, 2018, Hangzhou, China., 356-359(2018).

    [3] Qu Z, Lin L D, Guo Y et al. An improved algorithm for image crack detection based on percolation model[J]. IEEJ Transactions on Electrical and Electronic Engineering, 10, 214-221(2015).

    [4] Amhaz R, Chambon S, Idier J et al. Automatic crack detection on two-dimensional pavement images: an algorithm based on minimal path selection[J]. IEEE Transactions on Intelligent Transportation Systems, 17, 2718-2729(2016).

    [5] Xing Y F, Zhong L, Zhong X. An encoder-decoder network based FCN architecture for semantic segmentation[J]. Wireless Communications and Mobile Computing, 2020, 1-9(2020).

    [6] Mou L C, Hua Y S, Zhu X X. Relation matters: relational context-aware fully convolutional network for semantic segmentation of high-resolution aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 7557-7569(2020).

    [7] Li L F, Hu M. Method for small-bridge-crack segmentation based on generative adversarial network[J]. Laser & Optoelectronics Progress, 56, 101004(2019).

    [8] Cheng J, Ye L, Guo Y N et al. Ground crack recognition based on fully convolutional network with multi-scale input[J]. IEEE Access, 8, 53034-53048(2020).

    [9] Zou Q, Zhang Z, Li Q Q et al. DeepCrack: learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 28, 1498-1512(2019).

    [10] Zhang C, Chen Y. Object detection based on hard examples mining using residual network[J]. Laser & Optoelectronics Progress, 55, 101003(2018).

    [11] Xia K J, Yin H S, Qian P J et al. Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images[J]. IEEE Access, 7, 96349-96358(2019).

    [12] Li G, Gao Z Y, Zhang X C et al. Improved global convolutional network for pavement crack detection[J]. Laser & Optoelectronics Progress, 57, 081011(2020).

    [13] Sun M Y, Guo R H, Zhu J H et al. Roadway crack segmentation based on an encoder-decoder deep network with multi-scale convolutional blocks[C]. //2020 10th Annual Computing and Communication Workshop and Conference (CCWC), January 6-8, 2020, Las Vegas, NV, USA., 0869-0874(2020).

    [14] Kang D, Benipal S S, Gopal D L et al. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning[J]. Automation in Construction, 118, 103291(2020).

    [15] Li L F, Ma W F, Li L et al. Research on detection algorithm for bridge cracks based on deep learning[J]. Acta Automatica Sinica, 45, 1727-1742(2019).

    [16] Zhang L, Yang F, Daniel Z Y et al. Road crack detection using deep convolutional neural network[C]. //2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, AZ, USA., 3708-3712(2016).

    [17] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 3431-3440(2015).

    [18] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 6230-6239(2017).

    [19] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

    [20] Yao H B, Jiang J G, Qi M B et al. Image deblurring algorithm using Laplacian prior and bilateral filtering approach[J]. Transducer and Microsystem Technologies, 36, 139-142(2017).

    [21] Fu J, Liu J, Tian H J et al. Dual attention network for scene segmentation[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 3141-3149(2019).

    [22] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).

    [23] Zhou Z W, Siddiquee M M R, Tajbakhsh N et al. UNet++: a nested U-net architecture for medical image segmentation[M]. //Stoyanov D, Taylor Z, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science, 11045, 3-11(2018).

    [24] Zhang H, Dana K, Shi J P et al. Context encoding for semantic segmentation[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake, UT, USA., 7151-7160(2018).

    Liangfu Li, Nan Wang, Biao Wu, Xi Zhang. Segmentation Algorithm of Bridge Crack Image Based on Modified PSPNet[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2210001
    Download Citation