Convolutional neural networks performance evaluation applied to automated pavement crack detection

Authors

  • Francisco Dalla Rosa Federal University of Passo Fundo, Rio Grande do Sul – Brazil, https://orcid.org/0000-0001-6902-1430
  • Laura Dall'Igna Favretto Federal University of Passo Fundo, Rio Grande do Sul – Brazil,
  • Vítor Borba Rodrigues Federal University of Passo Fundo, Rio Grande do Sul – Brazil,
  • Nasir G. Gharaibeh Texas A&M University, Texas – Estados Unidos

DOI:

https://doi.org/10.14295/transportes.v28i5.2283

Keywords:

Convolutional neural network. Pavement management systems. Automated pavement crack detection. Computing vision.

Abstract

This research aims to analyze the performance of Convolutional Neural Network (CNN) as an automated tool applied to pavement surface crack detection. A group of pictures from different segments of chip seal pavement, acquired from photographic recording systems mounted on specific vehicles, was evaluated. An open-source machine learning library PyTorch available in the Python script language was applied to evaluate the images. The influence of three techniques used to process the pictures and the complexity of neural networks on the crack identification performance are discussed as well. The accuracy, precision, recall, and F1 score metrics were used to assess the neural network performance. The results show a good performance of the selected algorithm for pavement crack detection based on the observed metrics. Furthermore, it was found that the complexity of the neural network is an important factor that should be considered during the image classification process.

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References

Arpit, D.; S. Jastrzębski; N. Ballas; D. Krueger; E. Bengio; M. S. Kanwal; T. Maharaj; A. Fischer; A. Courville; Y. Bengio and S. Lacoste-Julien (2017). A Closer Look at Memorization in Deep Networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017. ArXiv - a repository of electronic preprints, 1–10. arXiv:1706.05394

Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools. Available at: http://drdobbs.com/opensource/184404319

Dalla Rosa, F.; N. G. Gharaibeh; E. G. Fernando and A. Wimsatt (2016). Quality Assurance for Automated and Semi-Automated Pavement Condition Surveys. International Conference on Transportation and Development 2016. p. 192–201. doi:10.1061/9780784479926.018

Dung, C. V. and L. D. Anh (2019). Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, v.99, p. 52–58. doi:10.1016/j.autcon.2018.11.028

Fan, Z.; S. Member; Y. Wu; J. Lu and W. Li (2018). Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network. ArXiv - a repository of electronic preprints, p. 1–9. arXiv:1802.02208

Haykin, S. (2009). Neural networks and learning machines. (3rd ed). Pearson, Ontario.

Khan, S.; H. Rahmani; S. A. A. Shah and M. Bennamoun (2018). A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer Vision, v.8 n.1, p. 1–207.DOI: 10.2200/s00822ed1v01y201712cov015

Koch, C.; K. Georgieva; V. Kasireddy; B. Akinci and P. Fieguth (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, v.29, n.2, p. 196–210. DOI: 10.1016/j.aei.2015.01.008

Li, S.; Y. Cao and H. Cai. (2017). Automatic Pavement-Crack Detection and Segmentation Based on Steerable Matched Filtering and an Active Contour Model. Journal of Computing in Civil Engineering, v.31, n.5, doi:10.1061/(ASCE)CP.1943-5487.0000695

Ong, G. P.; S. Noureldin and K. Sinha (2011). Technical report: Automated Pavement Condition Data Collection Quality Control, Quality Assurance, and Reliability. doi:10.5703/1288284314288

Osman, M. K.; M. H. M. Noor; A. Ibrahim; N. M. Tahir; N. M. Yusof and N. Z. Abidin (2019). Deep convolution neural network for crack detection on asphalt pavement. International Conference on Nanomaterials: Science, Engineering and Technology (ICoNSET) 2019. v. 1349, doi:10.1088/1742-6596/1349/1/012020

Paszke, A.; S. Gross; F. Massa; A. Lerer; J. Bradbury; G. Chanan; T. Killeen; Z. Lin; N. Gimelshein; L. Antiga; A. Desmaison; A. Kopf; E. Yang; Z. DeVito; M. Raison; A. Tejani; S. Chilamkurthy; B. Steiner; L. Fang; J. Bai and S. Chintala (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. H. Wallach, H. Larochelle, A. Beygelzimer, F. dtextquotesingle Alché-Buc, E. Fox, & R. Garnett (Eds), Advances in Neural Information Processing Systems 32 (p. 8024–8035). Available at: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

Pianucci, M. N.; C. S. Pitombo and A. L. Cunha (2019) Previsão da demanda por viagens domiciliares através de método sequencial baseado em população sintética e redes neurais artificiais. v. 27, n.4, p. 1–23. doi:10.14295/transportes.v27i4.1406

Pierce, L. M. and N. D. Weitzel (2019). Automated Pavement Condition Surveys. Automated Pavement Condition Surveys. Transportation Research Board, Washington, D.C. doi:10.17226/25513

Silva, W. R. L. and D. S. Lucena (2018). Concrete Cracks Detection Based on Deep Learning Image Classification. Proceedings, v.2, n.8. doi:10.3390/icem18-05387

Sun, Y.; E. Salari, and E. Chou (2009). Automated pavement distress detection using advanced image processing techniques. Proceedings of 2009 IEEE International Conference on Electro/Information Technology. EIT 2009. p. 373–377. doi:10.1109/EIT.2009.5189645

Zhang, A.; K. C. P. Wang; B. Li; E. Yang; X. Dai; Y. Peng; Y. Fei; Y. Liu; J. Q. Li and C. Chen (2017). Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network. Computer-Aided Civil and Infrastructure Engineering, v.32, n.10, p. 805–819. doi:10.1111/mice.12297

Zhang, L.; F. Yang; Y. D. Zhang and Y. J. Zhu (2016). Road crack detection using deep convolutional neural network. Proceedings - International Conference on Image Processing (ICIP). p. 3708–3712. doi:10.1109/ICIP.2016.7533052

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Published

2020-12-15

How to Cite

Dalla Rosa, F., Favretto, L. D., Rodrigues, V. B., & Gharaibeh, N. G. (2020). Convolutional neural networks performance evaluation applied to automated pavement crack detection. TRANSPORTES, 28(5), 267–279. https://doi.org/10.14295/transportes.v28i5.2283

Issue

Vol. 28 No. 5 (2020)

Section

Artigos

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