Computers and Concrete

  • 제26권5호
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  • Pages.411-420
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  • 2020
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Crack detection based on ResNet with spatial attention

  • Yang, Qiaoning (College of Information Science and Technology, Beijing University of Chemical Technology) ;
  • Jiang, Si (College of Information Science and Technology, Beijing University of Chemical Technology) ;
  • Chen, Juan (College of Information Science and Technology, Beijing University of Chemical Technology) ;
  • Lin, Weiguo (College of Information Science and Technology, Beijing University of Chemical Technology)
  • 투고 : 2020.05.28
  • 심사 : 2020.11.03
  • 발행 : 2020.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

Deep Convolution neural network (DCNN) has been widely used in the healthy maintenance of civil infrastructure. Using DCNN to improve crack detection performance has attracted many researchers' attention. In this paper, a light-weight spatial attention network module is proposed to strengthen the representation capability of ResNet and improve the crack detection performance. It utilizes attention mechanism to strengthen the interested objects in global receptive field of ResNet convolution layers. Global average spatial information over all channels are used to construct an attention scalar. The scalar is combined with adaptive weighted sigmoid function to activate the output of each channel's feature maps. Salient objects in feature maps are refined by the attention scalar. The proposed spatial attention module is stacked in ResNet50 to detect crack. Experiments results show that the proposed module can got significant performance improvement in crack detection.

키워드

참고문헌

  1. Ai, S., Jia, C. and Chen, Z. (2017), "Large-scale product classification via spatial attention based CNN learning and multi-class regression", Proceeding of International Conference on Multimedia Modeling (MMM), Reykjavik, Iceland, January, 176-188. https://doi.org/10.1007/978-3-319-51811-4_15.
  2. Cha, Y.J., Choi, W. and Buyukozturk, O. (2017), "Deep learning-based crack damage detection using convolutional neural networks", Comput.-Aid. Civil Inf., 32(5), 361-378. https://doi.org/10.1111/mice.12263.
  3. Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani, S. and Buyukozturk, O. (2018), "Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types", Comput.-Aid. Civil Inf., 33(9), 731-747. https://doi.org/10.1111/mice.12334.
  4. Chen, F.C. and Jahanshahi, M.R. (2018), "NB-CNN: deep learning-based crack detection using convolutional neural network and naive bayes data fusion", IEEE T. Ind. Electron., 65(5), 4392-4400. https://doi.org/10.1109/TIE.2017.2764844.
  5. Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W. and Chua, T.S. (2017), "SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning", Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, USA, July. https://doi.org/10.1109/CVPR. 2017.667.
  6. Corbetta, M. and Shulman, G.L. (2002), "Control of goal-directed and stimulus-driven attention in the brain", Nat. Rev. Neurosci., 3(3), 215-229. https://doi.org/10.1038/nrn755.
  7. Dorafshan, S., Thomas, R.J. and Maguire, M. (2018), "SDNET2018: an annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks", Data Brief, 21, 1664-1668. https://doi.org/10.1016/j.dib.2018.11.015.
  8. Gavilan, M., Balcones, D., Marcos, O., Llorca, D.F., Sotelo, M.A., Parra, I., Ocana, M., Aliseda, P., Yarza, P. and Amirola, A. (2011), "Adaptive road crack detection system by pavement classification", Sensor., 11(10), 9628-9657. https://doi.org/10.3390/s111009628.
  9. He, K., Zhang, X., Ren, S. and Sun, J. (2016), "Deep residual learning for image recognition", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), LAS VEGAS, USA, June. https://doi.org/10.1109/CVPR.2016.90.
  10. Hu, J., Shen, L., Albanie, S., Sun, G. and Wu, E. (2019), "Squeeze-and-excitation networks", IEEE T. Pattern Anal. Mach. Intell., 42(8), 2011-2023. https://doi.org/10.1109/TPAMI.2019. 2913372.
  11. Itti, L., Koch, C. and Niebur, E. (1998), "A model of saliency-based visual attention for rapid scene analysis", IEEE T. Pattern Anal. Mach. Intell., 20(11), 1254-1259. https://doi.org/10.1109/34.730558.
  12. Kang, D. and Cha, Y.J. (2018), "Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging", Comput.-Aid. Civil Inf., 33(10), 885-902. https://doi.org/10.1111/mice.12375.
  13. Kim, B. and Cho, S. (2018), "Automated vision-based detection of cracks on concrete surfaces using a deep learning technique", Sensor., 10(18), 3452. https://doi.org/10.3390/s18103452.
  14. Kim, H., Sim, S.H. and Cho, S. (2015), "Unmanned aerial vehicle (UAV)-powered concrete crack detection based on digital image processing", Proceedings of International Conference on Advances in Experimental Structural Engineering, Urbana-Champaign, USA, August.
  15. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012), "ImageNet classification with deep convolutional neural networks", Adv. Neural Inform. Process. Syst., 25(2), 1097-1105. https://doi.org/10.1145/3065386.
  16. LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998), "Gradient-based learning applied to document recognition", Proc. IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791.
  17. Lee, D., Kim, J. and Lee, D. (2019), "Robust concrete crack detection using deep learning-based semantic segmentation", Int. J. Aeronaut. Spaces, 2019(20), 287-299. https://doi.org/10.1007/s42405-018-0120-5.
  18. Li, H., Chen, G., Li, G. and Yu, Y. (2019), "Motion guided attention for video salient object detection", Proceeding of IEEE International Conference on Computer Vision (ICCV), Seoul, Korea, October. https://doi.org/10.1109/ICCV.2019.00737.
  19. Li, L.F., Ma, W.F., Li, L. and Lu, C. (2019), "Research on detection algorithm for bridge cracks based on deep learning", Acta Automatica Sinica, 45(9), 1727-1742. https://doi.org/10.16383/j.aas.2018.c170052.
  20. Lin, W., Sun, Y., Yang, Q. and Lin, Y. (2019), "Real-time comprehensive image processing system for detecting concrete bridges crack", Comput. Concrete, 23(6), 445-457. https://doi.org/10.12989/cac.2019.23.6.445.
  21. Mnih, V., Heess, N., Graves, A. and Kavukcuoglu, K. (2014), "Recurrent models of visual attention", Proceeding of Advance in Neural Information. Processing System, Montreal, Canada, December.
  22. Ozgenel, C.F. (2018), "Concrete crack images for classification", Mendeley Data, https://doi.org/10.17632/5y9wdsg2zt.1.
  23. Rensink, R.A. (2000), "The dynamic representation of scenes", J. Visual Cognition, 7, 17-42. https://doi.org/10.1080/135062800394667.
  24. Simonyan, K. and Zisserman, A. (2015), "Very deep convolutional networks for large-scale image recognition", Proceeding of 3rd International Conference on Learning Representations (ICLR), San Diego, USA, May.
  25. Su, T.C. and Yang, M. (2018), "Morphological segmentation based on edge detection-II for automatic concrete crack measurement", Comput. Concrete, 21(6), 727-739. https://doi.org/10.12989/cac.2018.21.6.727.
  26. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A. (2015), "Going deeper with convolutions", Proceedings of IEEE Computer Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June. https://doi.org/10.1109/CVPR.2015.7298594.
  27. Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X. and Tang, X. (2017), "Residual attention network for image classification", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, USA, July. https://doi.org/10.1109/CVPR.2017.683.
  28. Wang, P., Hu, Y., Dai, Y. and Tian, M. (2017), "Asphalt pavement pothole detection and segmentation based on wavelet energy field", Math. Probl. Eng., 2017(8), 1604130. https://doi.org/10.1155/2017/1604130.
  29. Woo, S., Park, J., Lee, J.Y. and Kweon, I.S. (2018), "CBAM: convolutional block attention module", Proceedings of European Conference on Computer Vision (ECCV), Munich, Germany, September.
  30. Xu, H., Su, X., Wang, Y., Cai, H., Cui, K. and Chen, X. (2019), "Automatic bridge crack detection using a convolutional neural network", Appl. Sci., 2019(9), 2867. https://doi.org/10.3390/app9142867.
  31. Xu, K., Ba, J.L., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R.S. and Bengio, Y. (2015), "Show, attend and tell: Neural image caption generation with visual attention", Proceedings of International Conference on Machine Learning (ICML), Lile, France, July.
  32. Yeum, C.M. and Dyke, S.J. (2015), "Vision-based automated crack detection for bridge inspection", Comput.-Aid. Civil Inf., 30(10), 759-770. https://doi.org/10.1111/mice.12141.
  33. Yin, W., Schutze, H., Xiang, B. and Zhou, B. (2016), "ABCNN: attention-based convolutional neural network for modeling sentence pairs", https://arxiv.org/abs/1512.05193.
  34. Zeiler, M.D. and Fergus, R. (2014), "Visualizing and understanding convolutional networks", Proceedings of European Conference on Computer Vision (ECCV), Zurich, Switzerland, September.
  35. Zhang, X., Wang, T., Qi, J., Lu, H. and Wang, G. (2018), "Progressive attention guided recurrent network for salient object detection", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, June. https://doi.org/10.1109/CVPR.2018.00081.
  36. Zhao, G., Wang, T. and Ye, J. (2014), "Surface shape recognition method for crack detection", J. Electron. Imag., 23(3), 1267-1276. https://doi.org/10.1117/1.JEI.23.3.033013.
  37. Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q. and Wang, S. (2019), "DeepCrack: learning hierarchical convolutional features for crack detection", IEEE T. Image Process., 28(3), 1498-1512. https://doi.org/10.1109/TIP.2018.2878966.