• Laser & Optoelectronics Progress
  • Vol. 56, Issue 10, 101004 (2019)
Liangfu Li and Min Hu*
Author Affiliations
  • School of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
  • show less
    DOI: 10.3788/LOP56.101004 Cite this Article Set citation alerts
    Liangfu Li, Min Hu. Method for Small-Bridge-Crack Segmentation Based on Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101004 Copy Citation Text show less
    References

    [1] Pang X R, Wang D P. Oblique crack monitoring of concrete beam with macro FBG strain sensor[J]. Laser & Optoelectronics Progress, 54, 120603(2017).

    [2] Qu L, Wang K R, Chen L L et al. Fast road detection based on RGBD images and convolutional neural network[J]. Acta Optica Sinica, 37, 1010003(2017).

    [3] Wang B, Wang X, Chen F et al. Pavement crack recognition based on aerial image[J]. Acta Optica Sinica, 37, 0810004(2017).

    [4] Li Q Q, Liu X L. Novel approach to pavement image segmentation based on neighboring difference histogram method. [C]∥2008 Congress on Image and Signal Processing, May 27-30, 2008, Sanya, Hainan, China. New York: IEEE, 792-796(2008).

    [5] Landstrom A, Thurley M J. Morphology-based crack detection for steelslabs[J]. IEEE Journal of Selected Topics in Signal Processing, 6, 866-875(2012). http://ieeexplore.ieee.org/document/6263264/

    [6] Lu Y Y, Lu C W, Tang C K. Onlinevideo object detection using association LSTM. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2363-2371(2017).

    [7] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2999-3007(2017).

    [8] Huang C, Chen P, Yang X et al. REDBEE: A visual-inertial drone system for real-time moving object detection. [C]∥2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 24-28, 2017, Vancouver, BC, Canada. New York: IEEE, 1725-1731(2017).

    [9] Zhao S S, He N. Road surface crack detection based on CNN[J]. Transducer and Microsystem Technologies, 36, 135-138(2017).

    [10] Jégou S, Drozdzal M, Vazquez D et al. The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 1175-1183(2017).

    [11] 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). http://www.ncbi.nlm.nih.gov/pubmed/28060704

    [12] Bell S, Zitnick C L, Bala K et al. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2874-2883(2016).

    [13] BenensonR, OmranM, HosangJ, et al.Ten years of pedestrian detection, what have we learned?[C]∥European Conference on Computer Vision, September 6-7 and 12, 2014, Zurich, Switzerland. Switzerland AG: Springer International Publishing, 2014: 613-627.

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

    [15] Chen X Z, Kundu K, Zhu Y K et al. 3d object proposals for accurate object class detection. [C]∥Proceedings of Advances in Neural Information Processing Systems 28 (NIPS 2015), December 7-12, 2015, Montreal, Canada. San Francisco: Margan Kaufmann, 424-432(2015).

    [16] Zhu J W, Liu W H, Yin J F et al. Infrared small target regions detection based on improved image complexity[J]. Laser & Optoelectronics Progress, 55, 101006(2018).

    [17] LiuW, AnguelovD, ErhanD, et al.SSD: single shot multibox detector[C]∥European conference on computer vision. Springer, October 11-14, 2016, Amsterdam, The Netherlands. Switzerland AG: Springer International Publishing, 2016: 21-37.

    [18] Yang F, Choi W, Lin Y Q. Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2129-2137(2016).

    [19] He K M, Zhang X Y, Ren S et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 37, 1904-1916(2015). http://ieeexplore.ieee.org/document/7005506

    [20] Li H X, Lin Z, Shen X H et al. A convolutional neural network cascade for face detection. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 5325-5334(2015).

    [21] Wu Y H, Liu Y L, Li J M et al. Traffic sign detection based on convolutional neural networks. [C]∥The 2013 International Joint Conference on Neural Networks (IJCNN), August 4-9, 2013, Dallas, TX, USA. New York: IEEE, 1-7(2013).

    [22] Goodfellow L J, Pouget A J, Mirza M et al. Generative adversarial nets. [C]// Proceedings of Advances in Neural Information Processing Systems 27 (NIPS 2014), December 8-13, 2014, Montreal, Canada. San Francisco: Margan Kaufmann, 2672-2680(2014).

    [23] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 105-114(2017).

    [24] Tsai Y H, Hung W C, Schulter S et al. Learning to adapt structured output space for semantic segmentation. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 7472-7481(2018).

    [25] Simon J, Michal D, David V et al. The one hundred layers tiramisu: fully convolutional denseNets for semantic segmentation. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 1175-1183(2017).

    [26] 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. New York: IEEE, 6230-6239(2017).

    Liangfu Li, Min Hu. Method for Small-Bridge-Crack Segmentation Based on Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101004
    Download Citation