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Spiral-Net with F1-Based Optimization for Image-Based Crack Detection

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11361))

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Abstract

Detecting cracks on concrete surface images is a key inspection for maintaining infrastructures such as bridge and tunnels. From the viewpoint of computer vision, the task of automatic crack detection poses two challenges. First, since the cracks are visually depicted by subtle patterns and also exhibit similar appearance to the other structural patterns, it is difficult to discriminatively characterize such less distinctive and finer defects. Second, the cracks are scarcely found, making the number of training samples for cracks significantly smaller than that of the other normal samples to be distinguished from the cracks. This is regarded as a class imbalance problem where the classifier is highly biased toward majority classes. In this study, we propose two methods to address these issues in the framework of deep learning for crack detection: a novel network, called Spiral-Net, and an effective optimization method to train the network. The proposed network is extended from U-Net to extract more detailed visual features, and the optimization method is formulated based on F1 score (F-measure) for properly learning the network even on the highly imbalanced training samples. The experimental results on crack detection demonstrate that the two proposed methods contribute to performance improvement individually and jointly.

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Notes

  1. 1.

    Actually, in the training, we divide the losses \(\tilde{L}\) and \(\bar{L}\) by the number of samples \(N=N_1+N_{-1}\), which is here omitted for simplicity.

  2. 2.

    Unfortunately, there is no analytic loss function that produces the derivative (11); see the supplementary material.

  3. 3.

    In the preliminary experiment, we confirmed that the optimization using the globally cumulative statistics does not provide any performance improvement.

References

  1. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. PAMI 33(5), 898–916 (2011)

    Article  Google Scholar 

  2. Bulo, S.R., Neuhold, G., Kontschieder, P.: Loss max-pooling for semantic image segmentation. In: CVPR, pp. 7082–7091 (2017)

    Google Scholar 

  3. Caesar, H., Uijlings, J.R.R., Ferrari, V.: Joint calibration for semantic segmentation. In: BMVC (2015)

    Google Scholar 

  4. Cha, Y.J., Choi, W., Büyüköztürk, O.: Deep learning-based cracking damage detection using CNNs. Comput. Aided Civ. Infrastruct. Eng. 32(5), 361–378 (2017)

    Article  Google Scholar 

  5. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: BMVC (2014)

    Google Scholar 

  6. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  7. Dong, Q., Gong, S., Zhu, X.: Class rectification hard mining for imbalanced deep learning. In: ICCV, pp. 1869–1878 (2017)

    Google Scholar 

  8. Fujita, Y., Hamamoto, Y.: A robust automatic crack detection method from noisy concrete surfaces. Mach. Vis. Appl. 22, 245–254 (2011)

    Article  Google Scholar 

  9. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)

    Article  Google Scholar 

  10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

    Google Scholar 

  11. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  Google Scholar 

  12. Huang, C., Li, Y., Loy, C.C., Tang, X.: Learning deep representation for imbalanced classification. In: CVPR, pp. 5375–5384 (2016)

    Google Scholar 

  13. Huang, G., Liu, Z., Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, pp. 2261–2269 (2017)

    Google Scholar 

  14. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR, pp. 5967–5976 (2017)

    Google Scholar 

  15. Jansche, M.: Maximum expected F-measure training of logistic regression models. In: HLT, pp. 692–699 (2005)

    Google Scholar 

  16. Jeatrakul, P., Wong, K.W., Fung, C.C.: Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010. LNCS, vol. 6444, pp. 152–159. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17534-3_19

    Chapter  Google Scholar 

  17. Khoshgoftaar, T.M., Golawala, M., Hulse, J.V.: An empirical study of learning from imbalanced data using random forest. In: ICTAI, pp. 310–317 (2007)

    Google Scholar 

  18. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)

    Google Scholar 

  19. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: ICCV, pp. 2999–3007 (2017)

    Google Scholar 

  20. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)

    Google Scholar 

  21. Maciejewski, T., Stefanowski, J.: Local neighborhood extension of smote for mining imbalanced data. In: ICDM, pp. 104–111 (2011)

    Google Scholar 

  22. Mani, I., Zhang, I.: KNN approach to unbalanced data distributions: a case study involving information extraction. In: Workshop on Learning from Imbalanced Datasets (2003)

    Google Scholar 

  23. Mohan, A., Poobal, S.: Crack detection using image processing: a critical review and analysis. Alexandria Eng. J. (2017). https://doi.org/10.1016/j.aej.2017.01.020

    Article  Google Scholar 

  24. Mostajabi, M., Yadollahpour, P., Shakhnarovich, G.: Feed-forward semantic segmentation with zoom-out features. In: CVPR, pp. 3376–3385 (2015)

    Google Scholar 

  25. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29

    Chapter  Google Scholar 

  26. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: ICCV, pp. 1520–1528 (2015)

    Google Scholar 

  27. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779–788 (2016)

    Google Scholar 

  28. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  29. Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: DeepContour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: CVPR, pp. 3982–3991 (2015)

    Google Scholar 

  30. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)

    Google Scholar 

  31. Taha, M.M.R., Noureldin, A., Lucero, J.L., Baca, T.J.: Wavelet transform for structural health monitoring: a compendium of uses and features. Struct. Health Monit. 5, 267–295 (2006)

    Article  Google Scholar 

  32. Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.: SVMs modeling for highly imbalanced classification. IEEE Trans. Syst. Man. Cybern. 39(1), 281–288 (2009)

    Article  Google Scholar 

  33. Ting, K.M.: A comparative study of cost-sensitive boosting algorithms. In: ICML, pp. 983–990 (2000)

    Google Scholar 

  34. Wojna, Z., et al.: The devil is in the decoder. In: BMVC (2017)

    Google Scholar 

  35. Xie, S., Tu, Z.: Holistically-nested edge detection. In: ICCV, pp. 1395–1403 (2015)

    Google Scholar 

  36. Xu, J., Schwing, A.G., Urtasun, R.: Learning to segment under various forms of weak supervision. In: CVPR, pp. 3781–3790 (2015)

    Google Scholar 

  37. Yamaguchi, T., Hashimoto, S.: Fast crack detection method for large-size concrete surface images using percolation-based image processing. Mach. Vis. Appl. 21, 797–809 (2010)

    Article  Google Scholar 

  38. Yang, Y.S., Yang, C.M., Huang, C.W.: Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis. Adv. Eng. Softw. 83, 99–108 (2015)

    Article  Google Scholar 

  39. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)

    Google Scholar 

  40. Zhang, L., Yang, F., Zhang, Y.D., Zhu, Y.J.: Road crack detection using deep convolution neural network. In: ICIP, pp. 2791–2799 (2016)

    Google Scholar 

  41. Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. Knowl. Data Eng. 18(1), 63–77 (2006)

    Article  MathSciNet  Google Scholar 

  42. Zou, Q., Cao, Y., Li, Q., Mao, Q., Wang, S.: CrackTree: automatic crack detection from pavement images. Pattern Recogn. Lett. 33(3), 227–238 (2012)

    Article  Google Scholar 

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Acknowledgment

The author thanks Takeshi Nagami, Hisashi Sato and Yohei Hayasaka for their great effort to build the crack dataset. This work is based on a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

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Authors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

    Takumi Kobayashi

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  1. Takumi Kobayashi
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Correspondence to Takumi Kobayashi .

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Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C. V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Supplementary material 1 (pdf 144 KB)

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Kobayashi, T. (2019). Spiral-Net with F1-Based Optimization for Image-Based Crack Detection. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-20887-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20886-8

  • Online ISBN: 978-3-030-20887-5

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