Road crack detection using convolutional neural network

Sharmad Bhat; Saish Naik; Mandar Gaonkar; Pradnya Sawant; Shailendra Aswale; Pratiksha Shetgaonkar

doi:10.17485/IJST/v14i10.1245

Article

Road crack detection using convolutional neural network

VIEWS 2468
PDF 1056

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i10.1245

Year: 2021, Volume: 14, Issue: 10, Pages: 881-891

Original Article

Road crack detection using convolutional neural network

Sharmad Bhat¹, Saish Naik¹, Mandar Gaonkar¹, Pradnya Sawant¹, Shailendra Aswale^1*, Pratiksha Shetgaonkar¹

¹Computer Department, SRIEIT, Goa University, Shiroda, 403103, India. Tel.: +91-9822161240

*Corresponding Author
Tel: +91-9822161240
Email: [email protected]

Received Date:13 August 2020, Accepted Date:28 February 2021, Published Date:02 April 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The proposed research work detects road cracks in a given set of images. In addition, it identifies the longitudinal type of crack in given crack image. Methods: The study mainly focuses on implementing a road crack detection technique using Convolutional Neural Networks. Findings: The proposed model is able to distinguish between crack and non-crack images and also able to classify the longitudinal crack from other given crack images. Novelty: Proposed road crack detection technique provides high accuracy compared to earlier standard techniques.

Keywords: Road crack detection; CNN (Convolutional Neural Networks); support vector machines (SVM); deep learning; classification; image processing

References

Mohan A, Poobal S. Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal. 2018;57(2):787–798. Available from: https://dx.doi.org/10.1016/j.aej.2017.01.020
Bang S, Park S, Kim H, Kim H. Encoder-decoder network for pixel-level road crack detection in black-box images. Computer-Aided Civil and Infrastructure Engineering. 2019;34(8):713–727. Available from: https://doi.org/10.1111/mice.12440
Gavilán M, Balcones D, Marcos O, Llorca DF, Sotelo MA, Parra I, et al. Adaptive Road Crack Detection System by Pavement Classification. Sensors. 2011;11(10):9628–9657. Available from: https://dx.doi.org/10.3390/s111009628
Bhat S, Naik S, Gaonkar M, Sawant P, Aswale S, Shetgaonkar P. A Survey On Road Crack Detection Techniques. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), India. p. 1–6.
Rodriguez‐Lozano FJ, León‐García F, Gámez‐Granados JC, Palomares JM, Olivares J. Benefits of ensemble models in road pavement cracking classification. Computer-Aided Civil and Infrastructure Engineering. 2020;35(11):1194–1208. Available from: https://dx.doi.org/10.1111/mice.12543
Li B, Wang KC, Zhang A, Yang E, Wang G. Automatic classification of pavement crack using deep convolutional neural network. International Journal of Pavement Engineering. 2020;21(4):457–463. Available from: https://doi.org/10.1080/10298436.2018.1485917
Sari Y, Prakoso PB, Baskara AR. Application of neural network method for road crack detection. TELKOMNIKA (Telecommunication Computing Electronics and Control). 2020;18(4). Available from: https://dx.doi.org/10.12928/telkomnika.v18i4.14825
Chambon S, Moliard JM. Automatic Road Pavement Assessment with Image Processing: Review and Comparison. International Journal of Geophysics. 2011;2011:1–20. Available from: https://dx.doi.org/10.1155/2011/989354
Subirats P, Dumoulin J, Legeay V, Barba D. Automation of pavement surface crack detection using the continuous wavelet transform. In: 2006 International Conference on Image Processing. (pp. 3037-3040) 2006.
Sy NT, Avila M, Begot S, Bardet JC. Detection of defects in road surface by a vision system. In: 14th IEEE Mediterranean Electrotechnical Conference. (pp. 847-851) 2008.
Oliveira H, Correia PL. Automatic road crack segmentation using entropy and image dynamic thresholding. In: 17th European Signal Processing Conference. (pp. 622-626) 2009.
Chambon S. Detection of road cracks with multiple images. International Joint Conference on Computer Vision Theory and Applications, VISAPP, France. 2010.
Nguyen TS, Begot S, Duculty F, Avila M. Free-form anisotropy: A new method for crack detection on pavement surface images. In: 18th IEEE International Conference on Image Processing. (pp. 1069-1072) 2011.
Oliveira H, Correia PL. Automatic Road Crack Detection and Characterization. IEEE Transactions on Intelligent Transportation Systems. 2013;14(1):155–168. Available from: https://dx.doi.org/10.1109/tits.2012.2208630
Zou Q, Cao Y, Li Q, MQ, Cracktree WS. Automatic crack detection from pavement images. Pattern Recognition Letters. 2012;33(3):227–238. Available from: https://doi.org/10.1016/j.patrec.2011.11.004
Oliveira H, Correia PL. CrackIT-An image processing toolbox for crack detection and characterization. In: 2014 IEEE international conference on image processing (ICIP). (pp. 798-802) 2014.
Avila M, Begot S, Duculty F, Nguyen TS. 2D image based road pavement crack detection by calculating minimal paths and dynamic programming. In: 2014 IEEE International Conference on Image Processing (ICIP). (pp. 783-787) 2014.
Amhaz R, Chambon S, Idier J, Baltazart V. A new minimal path selection algorithm for automatic crack detection on pavement images. In: 2014 IEEE International Conference on Image Processing (ICIP). (pp. 788-792) 2014.
Premachandra C, Premachandra HWH, Parape CD, Kawanaka H. Road crack detection using color variance distribution and discriminant analysis for approaching smooth vehicle movement on non-smooth roads. International Journal of Machine Learning and Cybernetics. 2015;6(4):545–553. Available from: https://dx.doi.org/10.1007/s13042-014-0240-6
Lins RG, Givigi SN. Automatic Crack Detection and Measurement Based on Image Analysis. IEEE Transactions on Instrumentation and Measurement. 2016;65(3):583–590. Available from: https://dx.doi.org/10.1109/tim.2015.2509278
Amhaz R, Chambon S, Idier J, Baltazart V. Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection. IEEE Transactions on Intelligent Transportation Systems. 2016;17(10):2718–2729. Available from: https://dx.doi.org/10.1109/tits.2015.2477675
Akarsu B, Karaköse M, Parlak K, Akin E, Sarimaden A. A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation. International Journal of Applied Mathematics, Electronics and Computers. 2016;p. 290. Available from: https://dx.doi.org/10.18100/ijamec.270546
Gopalakrishnan K, Khaitan SK, Choudhary A, Agrawal A. Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials. 2017;157:322–330. Available from: https://dx.doi.org/10.1016/j.conbuildmat.2017.09.110
Baltazart V, Nicolle P, Yang L. Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images. In: 2017 25th European Signal Processing Conference (EUSIPCO). 2017.
Hassan N, Mathavan S, Kamal K. Road crack detection using the particle filter. In: 2017 23rd International Conference on Automation and Computing (ICAC). (pp. 1-6) 2017.
Geethalakshmi SN. A survey on crack detection using image processing techniques and deep learning algorithms. International Journal of Pure and Applied Mathematics. 2018;118(8):215–220. Available from: https://acadpubl.eu/jsi/2018-118-7-9/articles/8/29.pdf
Mandal V, Uong L, Adu-Gyamfi Y. Automated road crack detection using deep convolutional neural networks. In: 2018 IEEE International Conference on Big Data (Big Data). (pp. 5212-5215) 2018.
Kim H, Ahn E, Shin M, Sim SH. Crack and noncrack classification from concrete surface images using machine learning. Structural Health Monitoring. 2019;18(3). Available from: https://doi.org/10.1177/1475921718768747
Augustaukas R, Lipnickas A. Pixel-wise Road Pavement Defects Detection Using U-Net Deep Neural Network. In: 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). (pp. 468-471) 2019.
Wu S, Fang J, Zheng X, Li X. Sample and Structure-Guided Network for Road Crack Detection. IEEE Access. 2019;7:130032–130043. Available from: https://doi.org/10.1109/ACCESS.2019.2940767
Fan R, Bocus MJ, Zhu Y, Jiao J, Wang L, Ma F, et al. Road crack detection using deep convolutional neural network and adaptive thresholding. In: 2019 IEEE Intelligent Vehicles Symposium (IV). (pp. 474-479) 2019.
Song W, Jia G, Jia D, Zhu H. Automatic Pavement Crack Detection and Classification Using Multiscale Feature Attention Network. IEEE Access. 2019;7:171001–171012. Available from: https://dx.doi.org/10.1109/access.2019.2956191
Dib J, Sirlantzis K, Howells G. A Review on Negative Road Anomaly Detection Methods. IEEE Access. 2020;8:57298–57316. Available from: https://dx.doi.org/10.1109/access.2020.2982220
Zhang L, Yang F, Zhang YD, Zhu YJ. Road crack detection using deep convolutional neural network. In: 2016 IEEE International Conference on Image Processing. .

Copyright

© 2021 Bhat et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)