Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

ARF-Crack: rotation invariant deep fully convolutional network for pixel-level crack detection

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Autonomous detection of structural defect from images is a promising, but also challenging task to replace manual inspection. With the development of deep learning algorithms, several studies have adopted deep convolutional neural networks (CNN) or fully convolutional networks (FCN) to detect cracks in pixel-level. However, a fundamental property of cracks, that they are rotation invariant, has never been exploited. Although the rotation-invariant property can be implicitly learned by data augmentation, the network needs more parameters to learn features of different orientations and thus tend to overfit the training data. In this study, a rotation-invariant FCN called ARF-Crack is proposed that utilizes the rotation-invariant property of cracks explicitly. The architecture of a state-of-the-art FCN called DeepCrack for pixel-level crack detection is adopted and revised where active rotating filters (ARFs) are used to encode the rotation-invariant property into the network. The proposed ARF-Crack is evaluated on several benchmark datasets including concrete cracks, pavement cracks and corrosion images. The experimental results show that the proposed ARF-Crack requires less number of network parameters and achieves the highest average precision values for all the benchmark datasets compared to other approaches. The proposed ARF-Crack has the potential of detecting other rotation-invariant defects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Abdel-Qader, I., Abudayyeh, O., Kelly, M.E.: Analysis of edge-detection techniques for crack identification in bridges. J. Comput. Civ. Eng. 17(4), 255–263 (2003). https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255)

    Article  Google Scholar 

  2. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder–decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  3. Bang, S., Park, S., Kim, H., Kim, H.: Encoder–decoder network for pixel-level road crack detection in black-box images. Comput. Aided Civ. Infrastruct. Eng. 34(8), 713–727 (2019)

    Article  Google Scholar 

  4. Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. arXiv:1206.5533 [cs] (2012)

  5. Cha, Y.J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural networks. Comput. Aided Civ. Infrastruct. Eng. 32(5), 361–378 (2017). https://doi.org/10.1111/mice.12263

    Article  Google Scholar 

  6. Chen, F.C., Jahanshahi, M.R.: NB-CNN: deep learning-based crack detection using convolutional neural network and naïve bayes data fusion. IEEE Trans. Ind. Electron. 65(5), 4392–4400 (2018)

    Article  Google Scholar 

  7. Chen, F.C., Jahanshahi, M.R., Wu, R.T., Joffe, C.: A texture-based video processing methodology using bayesian data fusion for autonomous crack detection on metallic surfaces. Comput. Aided Civ. Infrastruct. Eng. 32(4), 271–287 (2017). https://doi.org/10.1111/mice.12256

    Article  Google Scholar 

  8. Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54(12), 7405–7415 (2016)

    Article  Google Scholar 

  9. Cheng, H.D., Chen, J.R., Glazier, C., Hu, Y.G.: Novel approach to pavement cracking detection based on fuzzy set theory. J. Comput. Civ. Eng. 13(4), 270–280 (1999). https://doi.org/10.1061/(ASCE)0887-3801(1999)13:4(270)

    Article  Google Scholar 

  10. Cheng, H.D., Shi, X.J., Glazier, C.: Real-time image thresholding based on sample space reduction and interpolation approach. J. Comput. Civ. Eng. 17(4), 264–272 (2003). https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(264)

    Article  Google Scholar 

  11. Dieleman, S., De Fauw, J., Kavukcuoglu, K.: Exploiting cyclic symmetry in convolutional neural networks. arXiv preprint arXiv:1602.02660 (2016)

  12. Dieleman, S., Willett, K.W., Dambre, J.: Rotation-invariant convolutional neural networks for galaxy morphology prediction. Mon. Notices R. astron. soc. 450(2), 1441–1459 (2015)

    Article  Google Scholar 

  13. Eisenbach, M., Stricker, R., Seichter, D., Amende, K., Debes, K., Sesselmann, M., Ebersbach, D., Stoeckert, U., Gross, H.M.: How to get pavement distress detection ready for deep learning? A systematic approach. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2039–2047. IEEE (2017)

  14. Fan, Z., Wu, Y., Lu, J., Li, W.: Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network. arXiv preprint arXiv:1802.02208 (2018)

  15. Fujita, Y., Hamamoto, Y.: A robust automatic crack detection method from noisy concrete surfaces. Mach. Vis. Appl. 22(2), 245–254 (2010). https://doi.org/10.1007/s00138-009-0244-5

    Article  Google Scholar 

  16. Geusebroek, J.M., Smeulders, A.W., Geerts, H.: A minimum cost approach for segmenting networks of lines. Int. J. Comput. Vis. 43(2), 99–111 (2001)

    Article  Google Scholar 

  17. Jahanshahi, M.R., Chen, F.C., Joffe, C., Masri, S.F.: Vision-based quantitative assessment of microcracks on reactor internal components of nuclear power plants. Struct. Infrastruct. Eng. 13(8), 1013–1026 (2017)

    Article  Google Scholar 

  18. Jahanshahi, M.R., Masri, S.F., Padgett, C.W., Sukhatme, G.S.: An innovative methodology for detection and quantification of cracks through incorporation of depth perception. Mach. Vis. Appl. 24(2), 227–241 (2011). https://doi.org/10.1007/s00138-011-0394-0

    Article  Google Scholar 

  19. Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., Fieguth, P.: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv. Eng. Inform. 29(2), 196–210 (2015). https://doi.org/10.1016/j.aei.2015.01.008

    Article  Google Scholar 

  20. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  21. Liu, Y., Cheng, M.M., Hu, X., Wang, K., Bai, X.: Richer convolutional features for edge detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3000–3009 (2017)

  22. Liu, Y., Yao, J., Lu, X., Xie, R., Li, L.: DeepCrack: a deep hierarchical feature learning architecture for crack segmentation. Neurocomputing 338, 139–153 (2019)

    Article  Google Scholar 

  23. Liu, Z., Cao, Y., Wang, Y., Wang, W.: Computer vision-based concrete crack detection using U-net fully convolutional networks. Autom. Constr. 104, 129–139 (2019)

    Article  Google Scholar 

  24. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

  25. Marcos, D., Volpi, M., Tuia, D.: Learning rotation invariant convolutional filters for texture classification. In: International Conference on Pattern Recognition (ICPR), pp. 2012–2017 (2016)

  26. Nair, V., Hinton, G.E.: Rectified Linear Units Improve Restricted Boltzmann Machines. In: Proceedings 27th International Conference on Machine Learning (ICML’10) pp. 807–814 (2010)

  27. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520–1528 (2015)

  28. Qu, Z., Bai, L., An, S.Q., Ju, F.R., Liu, L.: Lining seam elimination algorithm and surface crack detection in concrete tunnel lining. J. Electron. Imaging 25(6), 063004 (2016)

    Article  Google Scholar 

  29. Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z.: Automatic road crack detection using random structured forests. IEEE Trans. Intell. Transp. Syst. 17(12), 3434–3445 (2016)

    Article  Google Scholar 

  30. Wang, K.C., Zhang, A., Li, J.Q., Fei, Y., Chen, C., Li, B.: Deep learning for asphalt pavement cracking recognition using convolutional neural network. Airfield Highw. Pavements 2017, 166–177 (2017)

    Google Scholar 

  31. Wang, X., Hu, Z.: Grid-based pavement crack analysis using deep learning. In: International Conference on Transportation Information and Safety (ICTIS), pp. 917–924 (2017)

  32. Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395–1403 (2015)

  33. Yamaguchi, T., Hashimoto, S.: Fast crack detection method for large-size concrete surface images using percolation-based image processing. Mach. Vis. Appl. 21(5), 797–809 (2009). https://doi.org/10.1007/s00138-009-0189-8

    Article  Google Scholar 

  34. Yang, F., Zhang, L., Yu, S., Prokhorov, D., Mei, X., Ling, H.: Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 21(4), 1525–1535 (2019)

    Article  Google Scholar 

  35. Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., Yang, X.: Automatic pixel-level crack detection and measurement using fully convolutional network. Comput. Aided Civ. Infrastruct. Eng. (2018). https://doi.org/10.1111/mice.12412

    Article  Google Scholar 

  36. Zhang, A., Wang, K.C., Li, B., Yang, E., Dai, X., Peng, Y., Fei, Y., Liu, Y., Li, J.Q., Chen, C.: Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comp. Aided Civ. Infrastruct. Eng. 32(10), 805–819 (2017)

    Article  Google Scholar 

  37. Zhang, K., Cheng, H., Zhang, B.: Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning. J. Comput. Civ. Eng. 32(2), 04018001 (2018)

    Article  MathSciNet  Google Scholar 

  38. Zhang, L., Yang, F., Zhang, Y.D., Zhu, Y.J.: Road crack detection using deep convolutional neural network. In: Proceedings 2016 IEEE International Conference on Image Processing (ICIP’16), pp. 3708–3712 (2016). 10.1109/ICIP.2016.7533052

  39. Zhou, Y., Ye, Q., Qiu, Q., Jiao, J.: Oriented response networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 519–528 (2017)

  40. Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., Wang, S.: DeepCrack: Learning hierarchical convolutional features for crack detection. IEEE Trans. Image Process. 28(3), 1498–1512 (2019)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Fu-Chen Chen

  2. Lyles School of Civil Engineering, and School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Mohammad R. Jahanshahi

Authors
  1. Fu-Chen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad R. Jahanshahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad R. Jahanshahi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, FC., Jahanshahi, M.R. ARF-Crack: rotation invariant deep fully convolutional network for pixel-level crack detection. Machine Vision and Applications 31, 47 (2020). https://doi.org/10.1007/s00138-020-01098-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-020-01098-x

Keywords

Search

Navigation