CNN-DFT Based Approach Applied to Image Inspection of Railcar Component: A Comparison with Machine Learning Methods
Keywords:railcar inspection, convolutional neural network, discrete Fourier transform, image classification
The railcar component inspection is one of the most critical tasks in railway maintenance. The use of image processing, coupled with machine learning has emerged as a solution for replacing current standard methodologies. The spectral analysis gives the frequency representation of a signal and has been largely used in signal processing tasks. In this sense, this work proposes the evaluation of the use of the discrete Fourier transform (DFT) in addition to the spatial representation image of railcar component for an automatic detector of defective parts performed by convolutional neural network (CNN) classification. The most appropriate combination of images of the spatial and frequency domains is compared to the histogram of oriented gradients (HOG) feature descriptor linked to the multilayer perceptron (MLP) and support vector machine (SVM) classification, where data augmentation is investigated to improve the classification performed by all approaches. A search is made for the parameters that best fit the MLP and SVM models for comparison with the proposed approach. The results are given in measure of accuracy in addition to accuracy boxplot, and it showed encouraging results in the combination of spatial image and DFT magnitude combined with data augmentation as CNN inputs, reaching an accuracy of 96.04% and demonstrating statistically to have a significant difference between the comparative methods.
Avriel, M. Nonlinear programming: analysis and methods. Courier Corporation, Mineola, New York, 2003.
Bishop, C. M. Pattern Recognition and Machine Learning. Information science and statistics. Springer, New York, USA, 2006.
Cha, Y.-J., Choi, W., Suh, G., Mahmoudkhani, S., and Büyüköztürk, O. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering 33 (9): 731–747, 2018.
Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:1405.3531 , 2014.
Cortes, C. and Vapnik, V. Support-Vector Networks. Machine Learning 20 (3): 273–297, 1995.
Dalal, N. and Triggs, B. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). IEEE, San Diego, CA, USA, 2005.
Gibert, X., Patel, V. M., and Chellappa, R. Deep multitask learning for railway track inspection. IEEE Transactions on Intelligent Transportation Systems 18 (1): 153–164, 2017.
Haidari, A. and Tehrani, P. H. Thermal load effects on fatigue life of a cracked railway wheel. Latin American Journal of Solids and Structures 12 (6): 1144–1157, 2015.
Han, J., Kamber, M., and Pei, J. Data mining: concepts and techniques. Morgan Kaufmann - Elsevier, 225 Wyman Street,Waltham, MA 02451, USA, 2011.
Hart, J. M., Resendiz, E., Freid, B., Sawadisavi, S., Barkan, C. P. L., and Ahuja, N. Machine vision using multi-spectral imaging for undercarriage inspection of railroad equipment. In Proceedings of the 8th World Congress on Railway Research, Seoul, Korea. WCRR, Seoul, Korea, 2008.
IWnicki, S. CRC Press, Boca Raton, pp. 548, 2006.
Juang, C.-F. and Chang, C.-M. Human body posture classification by a neural fuzzy network and home care system application. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 37 (6): 984–994, 2007.
Kingma, D. P. and Ba, J. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, Conference Track Proceedings. ICLR 2015, San Diego, CA, USA, 2014.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. Curran Associates Inc, New York, USA, pp. 1097–1105, 2012.
Macucci, M., Di Pascoli, S., Marconcini, P., and Tellini, B. Derailment detection and data collection in freight trains, based on a wireless sensor network. IEEE Transactions on Instrumentation and Measurement 65 (9): 1977–1987, 2016.
Odanovic, Z. Analysis of the railway freight car axle fracture. Procedia Structural Integrity vol. 4, pp. 56–63, 2017.
Park, B., Chen, Y. R., Nguyen, M., and Hwang, H. Characterizing multispectral images of tumorous, bruised, skin-torn, and wholesome poultry carcasses. Transactions of the ASAE 39 (5): 1933–1941, 1996.
Sakhare, K., Kulkarni, A., Kumbhakarn, M., and Kare, N. Spectral and spatial domain approach for fabric defect detection and classification. In 2015 international conference on industrial instrumentation and control (ICIC). IEEE, United States, pp. 640–644, 2015.
Samant, N. and Sonar, P. Mammogram Classification in Transform Domain. In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, Noida, Delhi-NCR, pp. 56–62, 2018.
Sokolova, M. and Lapalme, G. A systematic analysis of performance measures for classification tasks. Information Processing & Management vol. 45, pp. 427–437, 2009.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering 63 (7): 1455–1462, 2016.
Tao, Y., Muthukkumarasamy, V., Verma, B., and Blumenstein, M. A texture extraction technique using 2D-DFT and Hamming distance. In Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003. IEEE, Xi’an, China, pp. 120–125, 2003.