Abstract
Crack damage is commonly observed for civil structures and infrastructure in service. The recent years have witnessed an excessive utilization of deep learning models for realizing autonomous and machine-vision based crack detection. Given this trend, this paper recognizes two entwined challenges: the preparation of large-scale training data and the detection of simple crack damage amid complex scenes. To address them, a novel data augmentation technique is proposed considering crack characteristics in images for realizing deep transfer learning using very small datasets. Numerical experimentation is conducted based on two types of crack datasets (concrete structures and asphalt pavement), each of which has only tens of images containing complex scenes. When evaluating the performance, a sliding-window based rating scheme is proposed, which is much stricter than the conventional bounding-box based approach. Quantitative performance analysis shows the acceptable performance (e.g., an overall accuracy of 93.81%, an F-2 score of 74.4%, and a very high recall of 91% for the crack detection in concrete images). The result demonstrates the effectiveness of the proposed data augmentation method and the superior transferability if the transfer learning is carried out through a fully fine-tuned training process.
Similar content being viewed by others
References
Chien, C., Martin, W., Meyer, A., Aggarwal, J.: Detection of cracks on highway pavements. Interim Report Texas Univ Austin Center for Transportation Research (1983)
Haas, C., Hendrickson, C.: Computer-based model of pavement surfaces. Transp. Res. Rec. 1260, 91–98 (1990)
Ritchie, S.G.: Digital imaging concepts and applications in pavement management. J. Transp. Eng. 116(3), 287–298 (1990)
Abdel-Qader, I., Kelly, M.: Analysis of edge-detection techniques for crack identification in bridges. ASCE J. Comput. Civil Eng. 17(4), 255–263 (2003)
Hutchinson, T.C., Chen, Z.: Improved image analysis for evaluating concrete damage. J. Comput. Civil Eng. 20, 210–216 (2006)
Nishikawa, T., Yoshida, J., Sugiyama, T., Fujino, Y.: Concrete crack detection by multiple sequential image filtering. Comput.-Aided Civil Infrastruct. Eng. 27(1), 29–47 (2012)
Fujita, Y., Hamamoto, Y.: A robust automatic crack detection method from noisy concrete surfaces. Mach. Vis. Appl. 22(2), 245–254 (2011)
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vision 30(2), 117–156 (1998)
Chen, Z., Hutchinson, T.C.: Image-based framework for concrete surface crack monitoring and quantification. Adv. Civil Eng. (2010). https://doi.org/10.1155/2010/215295
Liu, Z., Suandi, S.A., Ohashi, T., Ejima, T.: Tunnel crack detection and classification system based on image processing. In: Machine Vision Applications in Industrial Inspection X. International Society for Optics and Photonics, pp. 145–152 (2002)
Mohan, A., Poobal, S.: Crack detection using image processing: a critical review and analysis. Alexandria Eng. J. 57(2), 787–798 (2018)
Chen, Z., Derakhshani, R., Halmen, C., Kevern, J.T.: A texture-based method for classifying cracked concrete surfaces from digital images using neural networks. In: The 2011 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 2632–2637 (2011)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 248–255 (2009)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in neural information processing systems, pp. 3320–3328 (2014)
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint arXiv:14053531 (2014)
Wang, J., Perez, L.: The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Netw. Vis. Recognit. (2017)
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). IEEE, pp. 3708–3712 (2016)
Cha, Y.-J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural networks. Comput.-Aided Civil Infrastruct. Eng. 32(5), 361–378 (2017). https://doi.org/10.1111/mice.12263
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 Civil Infrastruct. Eng. 33(9), 731–747 (2018)
Beckman, G.H., Polyzois, D., Cha, Y.-J.: Deep learning-based automatic volumetric damage quantification using depth camera. Autom. Constr. 99, 114–124 (2019). https://doi.org/10.1016/j.autcon.2018.12.006
Ali, R., Cha, Y.-J.: Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer. Constr. Build. Mater. 226, 376–387 (2019)
Dung, C.V.: Autonomous concrete crack detection using deep fully convolutional neural network. Autom. Constr. 99, 52–58 (2019)
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)
Ni, F., Zhang, J., Chen, Z.: Pixel-level crack delineation in images with convolutional feature fusion. Struct. Control Health Monit. 26(1), e2286 (2019)
Ni, F., Zhang, J., Chen, Z.: Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning. Comput.-Aided Civil Infrastruct. Eng. 34(5), 367–384 (2019)
Gao, Y., Mosalam, K.M.: Deep transfer learning for image-based structural damage recognition. Comput.-Aided Civil Infrastruct. Eng. 33(9), 748–768 (2018)
Choi, W., Cha, Y.: SDDNet: real-time crack segmentation. IEEE Trans. Industr. Electron. 67(9), 8016–8025 (2020). https://doi.org/10.1109/TIE.2019.2945265
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)
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Fan, Q., Brown, L., Smith, J.: A closer look at faster R-CNN for vehicle detection. In: 2016 IEEE Intelligent Vehicles Symposium (IV). IEEE, pp. 124–129 (2016)
Jiang, H.: Learned-Miller E Face detection with the faster R-CNN. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). IEEE, pp. 650–657 (2017)
Xu, Y., Yu, G., Wang, Y., Wu, X., Ma, Y.: Car detection from low-altitude UAV imagery with the faster R-CNN. J. Adv. Transp. (2017)
Zhang, L., Lin, L., Liang, X., He, K.: Is faster r-cnn doing well for pedestrian detection? In: European Conference on Computer Vision, pp. 443–457. Springer (2016)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. arXiv e-prints: arXiv:1311.2901 (2013)
Chen, Z.: Small crack-damage images with masks (2020)
Lindeberg, T.: Scale-Space Theory in Computer Vision, vol. 256. Springer, New York (2013)
Lowe, D.: Distinctive image features fromscale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: a method for focus-of-attention. Int. J. Comput. Vis. 11(3), 283–318 (1993)
Harris, C.G., Stephens, M.A.: combined corner and edge detector. In: Alvey Vision Conference. vol 50. Citeseer, pp. 10–5244 (1988)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)
Chen, Z.: Identifying Structural Damage from Images. UC San Diego, San Diego (2009)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia. ACM, pp. 675–678 (2014)
Saito, T., Rehmsmeier, M.: The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10(3), e0118432 (2015)
Chinchor, N.: The statistical significance of the MUC-4 results. In: Proceedings of the 4th Conference on Message Understanding. Association for Computational Linguistics, pp. 30–50 (1992)
Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ACM, pp. 233–240 (2006)
Acknowledgements
This material is partially based upon work supported by the National Science Foundation (NSF) under Award Number IIA-1355406. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tang, S., Chen, Z. Scale–Space Data Augmentation for Deep Transfer Learning of Crack Damage from Small Sized Datasets. J Nondestruct Eval 39, 70 (2020). https://doi.org/10.1007/s10921-020-00715-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10921-020-00715-z