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Deep Convolutional Neural Networks with Transfer Learning for Old Buildings Pathologies Automatic Detection

  • Tawfik MasrourEmail author
  • Ibtissam El Hassani
  • Mohammed Salim Bouchama
Conference paper
  • 27 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1104)

Abstract

The automatic detection of structure defects based on computer vision is evolving, especially with constant advances in Deep Convolutional Neural Network. Several image-processing methods have been proposed over the years based on Deep Learning. Nevertheless, the studies are mainly concerned with the crack damage and do not takes into account the other pathologies that can affect a surface structure such as Alkali-silica reaction (ASR), efflorescence, carbonation of concrete, friable plaster, water infiltration, and scaling. In this paper, we propose the method of pre-trained learning Deep Convolutional Neural Networks DCNN model with Transfer learning for the detection of seven classes of old building damage in Medina of Fez and Meknes in Morocco. The robustness of the proposed approach is tested on different architectures and a small set of images not used in learning and validation steps.

Keywords

Structure defects Computer vision Deep learning Transfer learning Artificial intelligence 

Notes

Acknowledgments

We thank our partners from Structural Engineering Research and Consulting Office “Structal Engineering Bureau d’études & Conseil”, Fez Agency 134 B Lot Riad Azzaitoun R.A.C, Etg1, Route Ain Chkef 30000 Fez-Morocco, and our partners from NBR-North Center Laboratory of Building and Public Works, 29 Rue Abdessalam Mestaoui, quartier Adarissa, VN Fez- Morocco, who provided insight, expertise and assistance with defects identification and relevance that greatly assisted our research.

This work was supported by CMCLI Project, funded by My Ismaïl University Meknes.

References

  1. 1.
    Fez Old Medina Revamp. Ministry of Communication and Culture. Electronic, Kingdom of Morocco. Sources (Web Publications), 15 April 2019. http://www.maroc.ma/en/royal-activities/fez-old-medina-revamp-unwavering-will-hm-king-preserve-ancient-city. Accessed 31 May 2019
  2. 2.
    Mohan, A., Poobal, S.: Crack detection using image processing: a critical review and analysis. Alexandria Eng. J. 57(2), 787–798 (2018)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., De Schutter, B.: Deep convolutional neural networks for detection of rail surface defects. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2584–2589. IEEE, July 2016Google Scholar
  6. 6.
    Zhang, L., Yang, F., Zhang, Y.D., Zhu, Y.J.: Road crack detection using deep convolutional neural network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3708–3712. IEEE, September 2016Google Scholar
  7. 7.
    Gopalakrishnan, K., Khaitan, S.K., Choudhary, A., Agrawal, A.: Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection. Constr. Build. Mater. 157, 322–330 (2017)CrossRefGoogle Scholar
  8. 8.
    Chen, F.C., Jahanshahi, M.R.: NB-CNN: deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion. IEEE Trans. Industr. Electron. 65(5), 4392–4400 (2017)CrossRefGoogle Scholar
  9. 9.
    Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani, S., Büyüköztürk, O.: Autonomous structural visual inspection using region based deep learning for detecting multiple damage types. Comput. Aided Civ. Infrastruct. Eng. 33(9), 731–747 (2018)CrossRefGoogle Scholar
  10. 10.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  11. 11.
    Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)Google Scholar
  12. 12.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  13. 13.
    Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, pp. 379–387 (2016)Google Scholar
  14. 14.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  15. 15.
    Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer, Cham, October 2016Google Scholar
  16. 16.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tawfik Masrour
    • 1
    Email author
  • Ibtissam El Hassani
    • 1
  • Mohammed Salim Bouchama
    • 1
  1. 1.Artificial Intelligence for Engineering Sciences Team, National School of Arts and CraftsMoulay Ismail UniversityMeknesMorocco

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