Pavement Crack Segmentation using a U-Net based Neural Network
Resumo
Cracks on the concrete surface are symptoms and precursors of structural degradation and hence must be identified and remedied. However, locating cracks is a time-consuming task that requires specialized professionals and special equipment. The use of neural networks for automatic crack detection emerges to assist in this task. This work proposes one U-Net based neural network to perform crack segmentation, trained with the Crack500 and DeepCrack datasets, separately. The U-Net used has seven contraction and seven expansion layers, which differs from the original architecture of four layers of each part. The IoU results obtained by the model trained with Crack500 was 71.03%, and by the model trained with DeepCrack was 86.38%.
Palavras-chave:
CNN, Crak500, DeepCrack, crack segmentation
Referências
D. Kang, S. S. Benipal, D. L. Gopal, and Y.-J. Cha, “Hybrid pixellevel concrete crack segmentation and quantification across complex backgrounds using deep learning,” Automation in Construction, vol. 118, p. 103291, 2020.
Y. Liu, J. Yao, X. Lu, R. Xie, and L. Li, “DeepCrack: A deep hierarchical feature learning architecture for crack segmentation,” Neurocomputing, vol. 338, pp. 139–153, 2019.
F. Yang, L. Zhang, S. Yu, D. Prokhorov, X. Mei, and H. Ling, “Feature pyramid and hierarchical boosting network for pavement crack detection,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, pp. 1525–1535, 2019.
A. Ellenberg, A. Kontsos, F. Moon, and I. Bartoli, “Bridge related damage quantification using unmanned aerial vehicle imagery,” Structural Control and Health Monitoring, vol. 23, no. 9, pp. 1168–1179, 2016.
A. Mohan and S. Poobal, “Crack detection using image processing: A critical review and analysis,” Alexandria Engineering Journal, vol. 57, no. 2, pp. 787–798, 2018.
H. Zakeri, F. M. Nejad, and A. Fahimifar, “Image based techniques for crack detection, classification and quantification in asphalt pavement: a review,” Archives of Computational Methods in Engineering, vol. 24, no. 4, pp. 935–977, 2017.
Q. Yang, W. Shi, J. Chen, and W. Lin, “Deep convolution neural network-based transfer learning method for civil infrastructure crack detection,” Automation in Construction, vol. 116, p. 103199, 2020.
M. Slónski, “A comparison of deep convolutional neural networks for image-based detection of concrete surface cracks,” Computer Assisted Methods in Engineering and Science, vol. 26, no. 2, pp. 105–112, 2019.
S. L. Lau, E. K. Chong, X. Yang, and X. Wang, “Automated pavement crack segmentation using U-Net-based convolutional neural network,” IEEE Access, vol. 8, pp. 114 892–114 899, 2020.
H. Li, J. Zong, J. Nie, Z. Wu, and H. Han, “Pavement crack detection algorithm based on densely connected and deeply supervised network,” IEEE Access, vol. 9, pp. 11 835–11 842, 2021.
Y. Wang, K. Song, J. Liu, H. Dong, Y. Yan, and P. Jiang, “Renet: rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks,” Measurement, vol. 170, p. 108698, 2021.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention. Springer, 2015, pp. 234–241.
P. Ren, Y. Xiao, X. Chang, P.-Y. Huang, Z. Li, X. Chen, and X. Wang, “A comprehensive survey of neural architecture search: Challenges and solutions,” arXiv preprint arXiv:2006.02903, 2020.
F. Wang, A. Eljarrat, J. Müller, T. R. Henninen, R. Erni, and C. T. Koch, “Multi-resolution convolutional neural networks for inverse problems,” Scientific reports, vol. 10, no. 1, pp. 1–11, 2020.
D. dos Santos, A. Silva, P. de Faria, B. Travençolo, and M. do Nascimento, “Impacts of color space transformations on dysplastic nuclei segmentation using CNN,” in Anais do XVI Workshop de Visão Computacional. SBC, 2020, pp. 6–11.
Y. Liu, J. Yao, X. Lu, R. Xie, and L. Li, “DeepCrack: A deep hierarchical feature learning architecture for crack segmentation,” Neurocomputing, vol. 338, pp. 139–153, 2019.
F. Yang, L. Zhang, S. Yu, D. Prokhorov, X. Mei, and H. Ling, “Feature pyramid and hierarchical boosting network for pavement crack detection,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, pp. 1525–1535, 2019.
A. Ellenberg, A. Kontsos, F. Moon, and I. Bartoli, “Bridge related damage quantification using unmanned aerial vehicle imagery,” Structural Control and Health Monitoring, vol. 23, no. 9, pp. 1168–1179, 2016.
A. Mohan and S. Poobal, “Crack detection using image processing: A critical review and analysis,” Alexandria Engineering Journal, vol. 57, no. 2, pp. 787–798, 2018.
H. Zakeri, F. M. Nejad, and A. Fahimifar, “Image based techniques for crack detection, classification and quantification in asphalt pavement: a review,” Archives of Computational Methods in Engineering, vol. 24, no. 4, pp. 935–977, 2017.
Q. Yang, W. Shi, J. Chen, and W. Lin, “Deep convolution neural network-based transfer learning method for civil infrastructure crack detection,” Automation in Construction, vol. 116, p. 103199, 2020.
M. Slónski, “A comparison of deep convolutional neural networks for image-based detection of concrete surface cracks,” Computer Assisted Methods in Engineering and Science, vol. 26, no. 2, pp. 105–112, 2019.
S. L. Lau, E. K. Chong, X. Yang, and X. Wang, “Automated pavement crack segmentation using U-Net-based convolutional neural network,” IEEE Access, vol. 8, pp. 114 892–114 899, 2020.
H. Li, J. Zong, J. Nie, Z. Wu, and H. Han, “Pavement crack detection algorithm based on densely connected and deeply supervised network,” IEEE Access, vol. 9, pp. 11 835–11 842, 2021.
Y. Wang, K. Song, J. Liu, H. Dong, Y. Yan, and P. Jiang, “Renet: rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks,” Measurement, vol. 170, p. 108698, 2021.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention. Springer, 2015, pp. 234–241.
P. Ren, Y. Xiao, X. Chang, P.-Y. Huang, Z. Li, X. Chen, and X. Wang, “A comprehensive survey of neural architecture search: Challenges and solutions,” arXiv preprint arXiv:2006.02903, 2020.
F. Wang, A. Eljarrat, J. Müller, T. R. Henninen, R. Erni, and C. T. Koch, “Multi-resolution convolutional neural networks for inverse problems,” Scientific reports, vol. 10, no. 1, pp. 1–11, 2020.
D. dos Santos, A. Silva, P. de Faria, B. Travençolo, and M. do Nascimento, “Impacts of color space transformations on dysplastic nuclei segmentation using CNN,” in Anais do XVI Workshop de Visão Computacional. SBC, 2020, pp. 6–11.
Publicado
22/11/2021
Como Citar
GALINA, Raido Lacorte; MELO, Thadeu Pezzin; KOMATI, Karin Satie.
Pavement Crack Segmentation using a U-Net based Neural Network. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 76-81.
DOI: https://doi.org/10.5753/wvc.2021.18893.
