Automatic Pavement Crack Recognition Based on BP Neural Network

  • Li Li 1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University Shanghai 201804, China 2. Jiangxi Ganyue Expressway Co., Ltd Nanchang 330025, China
  • Lijun Sun Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University Shanghai 201804, China
  • Guobao Ning School of Automotive Studies Tongji University Shanghai 201804, China
  • Shengguang Tan Jiangxi Ganyue Expressway Co., Ltd Nanchang 330025, China
Keywords: crack detection, background correction, image processing, image recognition, BP neural network

Abstract

A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN) is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN) in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application.

References

Lee, H., Oshima, H.: New crack-imaging procedure using spatial autocorrelation function. Journal of Transportation Engineering, 1994; 120(2):206-228

Lee, H.D. Kim, J.J.: Development of a manual crack quantification and automated crack measurement system. Report, Project TR- 457, University of Iowa, 2005

Wang, C.F., Sha, A.M.: Pavement crack classification based on chain code. In Proceedings of 7th International Conference on Fuzzy Systems and Knowledge Discovery, IEEE, 2010; 593-597

Lee, B.J.: Development of an integrated digital pavement imaging and neural network system. PHD Dissertation, USA: The University of Iowa, 2001

Mustaffar, M., Ling, T.C., Puan, O.C.: Automated pavement imaging program (APIP) for pavement cracks classification and quantification - a photogrammetric approach. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2008; 37 (B4):367-372

Sorncharean, S., Phiphobmongkol, S.: Crack detection on asphalt surface image using enhanced grid cell analysis. In Proceedings of 4th IEEE International Symposium on Electronic Design, Test & Application, 2008; 49-54

Li L, Sun, L.J., Tan, S.G., Ning, G.B.: Line-structured light image processing method for pavement rut detection. Journal of Tongji University (Natural Science), 2013; 41(5):710-715

Cheng, H.D., Miyojim, M.: Novel system for automatic pavement distress detection. Journal of Computing in Civil Engineering, 1998 July; 145-152

Gao, J.Z., Ren, M.W., Yang, J.Y.: A practical and fast method for non-uniform illumination correction. Journal of Image and Graphics, 2002; 7A (6):548-552

Koutsopoulos, H.N., Downey, A.B.: Primitive-based classification of pavement cracking images. Journal of Transportation Engineering, 1993; 119(3):402-418

Li L, Sun, L.J., Chen, Z.: Modified background correction algorithms for pavement distress images. Journal of Tongji University (Natural Science), 2011; 39(1):79-84

Otsu, N.: A threshold selection method for gray level histograms. Transactions on Systems, Man and Cybernetics, IEEE, 1979; 9(1):62-66

Koutsopoulos, H.N., Sanhouri, I.E.: Methods and algorithms for automated analysis of pavement images. TRB1311, 1991; 103-111

Rosenfeld, A., Smith, R.C.: Thresholding using relaxation. Pattern Analysis and Machine Intelligence, IEEE Transactions, 1981; 3(5):598-606

Cheng, H.D.: Automated real-time pavement distress detection using fuzzy logic and neural network. In Proceedings of SPIE, 1996; 2946:140-151

Chen, G.: The fisher criterion function method of image thresholding. Chinese Journal of Scientific Instrument, 2003; 24(6):564-567

Li, L., Sun, L.J., Chen, Z.: An edge detection procedure designed for pavement images. Journal of Tongji University (Natural Science), 2011; 39(5):688-692

Maini, R., Aggarwal, H.: Study and comparison of various image edge detection techniques. International Journal of Image-processing, 2009; 3(1):1-12

Fukuhara, T., Terada, K., Nagao, M., Kasahara, A., Ichihashi, S.: Automatic pavement-distress-survey system. Journal of Transportation Engineering, 1990; 116(3):280-286

Oliverira, H., Correia, P.L.: Identifying and retrieving distress images from road pavement surveys. In Proceedings of ICIP, 2008; 57-60

Lee, B.J.: Development of an integrated digital pavement imaging and neural network system. PHD Dissertation, USA: The University of Iowa, 2001

Yu, B., Yang, Z.Z., Yao, B.Z.: Bus arrival time prediction using support vector machines. Journal of Intelligent Transportation Systems, 2006; 10(4):151-158

Yao, B.Z., Hu, P., Lu, X.H., Gao, J.J., Zhang, M.H.: Transit network design based on travel time reliability. Transportation Research Part C, 2013; DOI:10.1016/j.trc.2013.12.005

Yu B, William, H.K.L., Mei, L.T.: Bus arrival time prediction at bus stop with multiple routes. Transportation Research Part C, 2011, 19(6):1157-1170

Yu, B., Yang, Z.Z., Chen, K., Yu, B.: Hybrid model for prediction of bus arrival times at next station. Journal of Advanced Transportation, 2010; 44(3):193-204

Yu, B., Ye, T., Tian, X.M., Ning, G.B., Zhong, S.Q.: Bus travel-time prediction with forgetting factor. Journal of Computing in Civil Engineering, [Internet]. 2012 November [cited 2013 May 14];[about 27pp.]. Available from: http://ascelibrary.org/doi/pdf/10.1061/(ASCE)CP.1943-5487.0000274

Chien, S.J., Ding, Y., Wei, C.: Dynamic bus arrival time prediction with artificial neural networks. Journal of Transportation Engineering, 2002; 128(5):429-438

Chen, M., Liu, X., Xia, J., Chien, S.J.: A dynamic bus arrival time prediction model based on APC data. Computer-Aided Civil and Infrastructure Engineering, 2004; 19(5):364-376

Jeong, R., Rilett, L.R.: Bus arrival time prediction using artificial neural network model. In Proceedings of 7th International IEEE Conference on Intelligent Transportation Systems, 2004; 988-993

Basheer, I.A., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiol Methods, 2000; 43(1):3-31

Zhang, L., Luo, J.H., Yang, S.Y.: Forecasting box office revenue of movies with BP neural network. Expert Systems with Applications, 2009; 36(3):6580-6587

Wong, W.K., Yuen, C.W.M., Fan, D.D., Chan, L.K., Fung, E.H.K.: Stitching defect detection and classification using wavelet transform and BP neural network. Expert Systems with Applications, 2009; 36(2):3845-3856

Lippmann, R.P.: An introduction to computing with neutral networks. IEEE ASSP Magazine, 1987; 4(2):4-22

Cybenko, G.: Approximation by superpositions of a sigmoid function. Mathematics of Control, Signals and Systems, 1989; 2:303-314

Karsoliya, S.: Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 2012; 3(6):713-717

Boger, Z., Guterman, H.: Knowledge extraction from artificial neural network models. In Proceedings of IEEE Systems, Man, and Cybernetics Conference, 1997; 4:3030-3035

Berry, M.J.A., Linoff, G.: Data mining techniques. NY: John Wiley & Sons, 1997

Zhou, J.: Automated pavement inspection based on wavelet analysis. PHD Dissertation, USA: Stony Brook University, 2004

Li, N.N., Hou, X.D., Yang, X.Y., Dong, Y.F.: Automation recognition of pavement surface distress based on support vector machine. In Proceedings of 2nd International Conference on Intelligent Networks and Intelligent Systems, 2008: 346-349

How to Cite
1.
Li L, Sun L, Ning G, Tan S. Automatic Pavement Crack Recognition Based on BP Neural Network. Promet - Traffic&Transportation. 1;26(1):11-2. DOI: 10.7307/ptt.v26i1.1477
Section
Articles