[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3837/tiis.2020.12.008

Railroad Surface Defect Segmentation Using a Modified Fully Convolutional Network

Kim, Hyeonho (Department of Computer Science & Information Engineering Korea National University of Transportation)
Lee, Suchul (Department of Computer Science & Information Engineering Korea National University of Transportation)
Han, Seokmin (Department of Computer Science & Information Engineering Korea National University of Transportation)

Publication Information

KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.12, 2020 , pp. 4763-4775 More about this Journal

Abstract

This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

Keywords

Artificial Intelligence; Pattern Recognition; Computer Vision System;

Citations & Related Records

Reference

1	S.Yanan, Z. Hui, L. Li, and Z. Hang, "Rail surface defect detection method based on YOLOv3 deep learning networks," 2018 Chinese Automati on Congress (CAC), pp. 1563-15668, Dec. 2018.
2	J. Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
3	Z. Liang, H. Zhang, L. Liu, Z. He, and K. Zheng, "Defect detection of rail surface with deep convolutional neural networks," in Proc. of 2018 13th World Congress on Intelligent Control and Automation (WCICA), July 2018.
4	V. Badrinarayanan, A. Kendall, and R. Cipolla, "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481-2495, 2017. DOI
5	J. Gan, Q. Li, J. Wang, and H. Yu, "A Hierarchical Extractor-Based Visual Rail Surface Inspection System," IEEE Sensors Journal, vol. 17, no. 23, pp. 7935-7944, 1 Dec. 1, 2017. DOI
6	M. D. Zeiler, D.Krishnan, G. W. Taylor, and R. Fergus, "Deconvolutional networks," in Proc. of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2582-2535, June 2010.
7	A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet classification with deep convolutional neural networks ," NIPS, vol. 60, no. 6, 2012.
8	K. Simonyan and A. Zisserman. "Very deep convolutional networks for large-scale image recognition," ICLR, 2015.
9	D. Kingma and J. Ba, "Adam: A method for stochastic optimization," in Proc. of Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015.
10	Z. He, U. Wang, F. Yin, and J. Liu , "Surface defect detection for high-speed rails using an inverse PM diffusion model," Sensor Review, 2016.
11	D. Soukup and R. Huber-Mork, "Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images," International Symposium on Visual Computing, vol. 8887, pp. 668-677, 2014.
12	W. Zhao, Y. Fu, X. Wei, and H. Wang, "An improved image semantic segmentation method based on superpixels and conditional random fields," 2018 Applied Sciences, 2018.
13	O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Proc. of 2015 International Conference on Medical image computing and computer-assisted intervention, vol 9351, 2015.
14	H. Darwin and Schafer, "Effect of train length on railroad accidents and a quantitative analysis of factors affecting broken rails," B.S. thesis, Dept. Civil Engineering, Univ. of Illinois at UrbanaChampaign, Urbana, IL, USA, 2008.
15	N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," 2014 Journal of Machine Learning Research, vol. 15, no. 1, 2014.
16	H. Zhang, X. Jin, Q. M. J. Wu, Y. Wang, Z. He, and Y. Yang, "Automatic visual detection system of railway surface defects with curvature filter and improved gaussian mixture model," IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 7, pp. 1593-1608, July 2018. DOI
17	M. Yongzhi, B. Xiao, J. Dang, B. Yue, and T. Cheng, "Real time detection system for rail surface defects based on machine vision," EURASIP Journal on Image and Video Processing, 2018.
18	Y. Wu, Y. Qin, Z. Wang, and L. Jia, "A UAV-based visual inspection method for rail surface defects," 2018 Applied sciences, vol. 8, no. 7, 2018.
19	H. Yuan, H. Chen, S. Liu, J. Lin, and X. Luo, "A Deep Convolutional Neural Network for Detection of Rail Surface Defect," in Proc. of 2019 IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1-4, 2019.
20	M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, "Mobilenetv2: Inverted residuals and linear bottlenecks," in Proc. of the IEEE conference on computer vision and pattern recognition, vol. 1, 2018.
21	S. Faghih-Roohi, S. Hajizadeh, A. Nunez, R. Babuska, and B. De Schutter, "Deep convolutional neural networks for detection of rail surface defects," in Proc. of 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2584-2589, 2016. .
22	E. Shelhamer, J. Long, and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 640-651, Apr. 2017. DOI
23	R. Andersson, "Surface defects in rails: potential influence of operational parameters on squat initiation," Thesis for the degree of licentiate of engineering/Department of Applied Mechanics, Chalmers University of Technology, 2015.
24	X. Giben, V. M. Patel, and R. Chellappa, "Material classification and semantic segmentation of railway track images with deep convolutional neural networks," in Proc. of 2015 IEEE International Conference on Image Processing (ICIP), Sep. 2015.