An in-depth assessment of convolutional neural networks for rail surface defect detection
DOI:
https://doi.org/10.33448/rsd-v11i8.30252Keywords:
Rail inspection; Squat; CNN.Abstract
The consistent monitoring of rails is based on correctly identifying defects to support corrective measures. Recently, convolutional neural networks (CNN), a deep learning method, have been providing outstanding results for the automatic detection of defects. However, several aspects of CNN-based approaches such as network architecture, transfer learning and processing time remains not fully understood. In this work, we performed an in-depth assessment of ten widely used CNN models with the objective of finding the one with the best performance in identifying defects in rail surface images. The classification results are promising, reaching an average accuracy of 83.7% on detection of mild defects and squat. The Inceptionv3 network provided the best results by correctly identifying 92% of images with severe squat defects.
References
Badrinarayanan, V., & Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39, 2481–2495.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 1251–1258.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition, IEE, 248–255.
Demuth H. B. Neural network toolbox; for use with MATLAB; computation, visualization, programming; user’s guide, version 4. [S.l.], 2000. Disponível em: <http://cda.psych.uiuc.edu/matlab_pdf/nnet.pdf>.
Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., & De Schutter, B. (2016). Deep convolutional neural networks for detection of rail surface defects, in: 2016 International joint conference on neural networks (IJCNN), IEEE, 2584–2589.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K.Q. (2017). Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 4700–4708.
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., & Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360.
Jamshidi, A., Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., Dollevoet, R., Li, Z., & De Schutter, B. (2017). A big data analysis approach for rail failure risk assessment. Risk Analysis 37, 1495–1507.
Kim, H., Lee, S., & Han, S. (2020). Railroad surface defect segmentation using a modified fully convolutional network. KSII Transactions on Internet & Information Systems 14.
Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks, in: Advances in neural information processing systems, 1097–1105.
Liang, Z., Zhang, H., Liu, L., He, Z., & Zheng, K. (2018). Defect detection of rail surface with deep convolutional neural networks, in: 2018 13th World Congress on Intelligent Control and Automation (WCICA), IEEE, 1317–1322.
Lin, C., Li, L., Luo, W., Wang, K.C., & Guo, J. (2019). Transfer learning based traffic sign recognition using inception-v3 model. Periodica Polytechnica Transportation Engineering 47, 242–250.
Loram. (2018). Riv. URL: https://www.loram.com/products/ quality-management/riv. (accessed 28 October 2019).
MRS. (2008). MRS Logística S.A. Guia de Identificação de Defeitos e Fraturas em Trilhos.
Ponti, M.A., Ribeiro, L.S.F., Nazare, T.S., Bui, T., & Collomosse, J., (2017). Everything you wanted to know about deep learning for computer vision but were afraid to ask, in: 2017 30th SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T), IEEE, 17–41.
Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Rodrigues, Paulo Cézar Lobo. Detecção de anomalias em trilho utilizando visão computacional. Instituto Federal do Espírito Santo, Programa de Pós-graduação em Tecnologias Sustentáveis, Vitória,2020.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 4510–4520.
Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R.M. (2016). Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298.
Silva, P. et al.(2014). Use of artificial neural networks and geographic objects to classify remote sensing images. CERNE, Lavras, v. 20, n. 2, 267-276.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 1–9.
Tan, M., & Quoc, V.L. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. arxiv e-prints, art. arXiv preprint arXiv:1905.119461.
Yanan, S., Hui, Z., Li, L., & Hang, Z. (2018). Rail surface defect detection method based on yolov3 deep learning networks, in: 2018 Chinese Automation Congress (CAC), IEEE, 1563–1568.
Yuan, H., Chen, H., Liu, S., Lin, J., & Luo, X. (2019). A deep convolutional neural network for detection of rail surface defect, in: 2019 IEEE Vehicle Power and Propulsion Conference (VPPC), IEEE, 1–4.
Zhandong Yuan, Shengyang Zhu, Xuancheng Yuan, Wanming Zhai. (2021). Vibration-based damage detection of rail fastener clip using convolutional neural network: Experiment and simulation. Engineering Failure Analysis. Volume 119.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Rebeca Alves da Silva Lemos Passos; Matheus Pinheiro Ferreira; Ben-Hur de Albuquerque e Silva; Luiz Antonio Silveira Lopes; Hugo Ribeiro; Romero Pereira dos Santos
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
