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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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General Research Article

Research on Crack Detection Algorithm of Mining Car Baffle

Author(s): Weiwei Li* and Fanlei Yan

Volume 15, Issue 4, 2022

Published on: 18 September, 2020

Article ID: e220322186078 Pages: 9

DOI: 10.2174/2666255813999200918143531

Price: $65

Abstract

Introduction: Image processing technology is widely used for crack detection. This technology is used to build a data acquisition system and uses computer vision technology for image analysis. Because of its simplicity in processing, many of the image processing detection methods were proposed. It is relatively easy to deploy and has a low cost.

Methods: The heterogeneity of the external light usually changes the authenticity of each target in the image, which will seriously cause the experiment to fail. At this time, the image needs to be processed by the gamma transform. Based on the analysis of the characteristics of the image of the mine car baffle, this paper improves the Gamma transform and uses the it to enhance the image.

Results: It can be concluded that the algorithm in this paper can accurately detect crack areas with an actual width greater than 1.2 mm, and the error between the detected crack length and the actual length is between (-2, 2) mm. In practice, this error is completely acceptable.

Discussion: To compare the performance of a new crack detection method with the existing methods. The two most well-known traditional methods, Canny and Sobel edge detection, are selected. Although the Sobel edge detection provides some crack information. The texture of the surface of the mine cart baffle detected has caused great interference to the crack identification.

Conclusion: If the cracks appearing on the mine car baffle are not found in time, they often cause accidents. Therefore, effective crack detection must be performed. If a manual inspection is adopted for crack detection, it will be labor-intensive and easy to miss inspection. In order to reduce the labor of crack detection of mine cars and improve the accuracy of detection, this paper, based on the detection platform built, performs pre-processing, image enhancement, and convolution operations on the collected crack images of the mine car baffle.

Keywords: Car baffle, crack detection, image analysis, gamma transform, gradient filtering, computer vision technology.

Graphical Abstract

[1]
J. Chaki, and N. Dey, "J. Chaki, and N. Dey, “Pixel Brightness Transformation Techniques,” in A Beginner’s Guide to Image Preprocessing Techniques, Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academicdivision of T&F Informa, plc, 2019. | Series: Intelligent signalprocessing and data analysis: CRC Press,", 2018, pp. 13–24.
[http://dx.doi.org/10.1201/9780429441134]
[2]
J. Chaki, and N. Dey, “Spatial Interrelation Shape Features,” in A Beginner’s Guide to Image Shape Feature Extraction Techniques., CRC Press, 2019, pp. 65-79.
[http://dx.doi.org/10.1201/9780429287794]
[3]
B. Chakraborty, S. Bhattacharyya, and S. Chakraborty, "Generative model based video shot boundary detection for automated surveillance", Int. J. Ambient Comput. Intell., vol. 9, no. 4, pp. 69-95, 2018.
[http://dx.doi.org/10.4018/IJACI.2018100105]
[4]
S.N. Yu, J.H. Jang, and C.S. Han, "Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel", Autom. Construct., vol. 16, no. 3, pp. 150-159, 2007.
[http://dx.doi.org/10.1016/j.autcon.2006.05.003]
[5]
R.S. Adhikari, O. Moselhi, and A. Bagchi, "Image-based retrieval of concrete crack properties for bridge inspection", Autom. Construct., vol. 39, pp. 180-194, 2014.
[http://dx.doi.org/10.1016/j.autcon.2013.06.011]
[6]
R.G. Lins, and S.N. Givigi, "Automatic crack detection and measurement based on image analysis", IEEE Trans. Instrum. Meas., vol. 65, no. 3, pp. 583-590, 2016.
[http://dx.doi.org/10.1109/TIM.2015.2509278]
[7]
R. Medina, J. Llamas, J. Gómez-García-Bermejo, E. Zalama, and M.J. Segarra, "Crack detection in concrete tunnels using a gabor filter invariant to rotation", Sensors (Basel), vol. 17, no. 7, p. 1670, 2017.
[http://dx.doi.org/10.3390/s17071670] [PMID: 28726746]
[8]
R.S. Lim, H.M. La, and W.H. Sheng, "A robotic crack inspection and mapping system for bridge deck maintenance", IEEE Trans. Autom. Sci. Eng., vol. 11, no. 2, pp. 367-378, 2014.
[http://dx.doi.org/10.1109/TASE.2013.2294687]
[9]
H. Zakeri, F.M. Nejad, and A. Fahimifar, "Image based techniques for crack detection, classification and quantification in asphalt pavement: A review", Arch. Comput. Methods Eng., vol. 24, no. 4, pp. 1-43, 2016.
[10]
S. Mokhtari, L.L. Wu, and H.B. Yun, "Statistical selection and interpretation of imagery features for computer vision-based pavement crack–detection systems", J. Perform. Constr. Facil., vol. 31, no. 5, pp. 1-10, 2017.
[http://dx.doi.org/10.1061/(ASCE)CF.1943-5509.0001006]
[11]
Y.J. Cha, and W. Choi, "Deep learning-based crack damage detection using convolutional neural networks", Comput. Aided Civ. Infrastruct. Eng., vol. 32, no. 5, pp. 361-378, 2017.
[http://dx.doi.org/10.1111/mice.12263]
[12]
Y.J. Cha, W. Choi, G. Suh, S. Mahmoudkhani, and O. Büyüköztürk, "Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types", Comput. Aided Civ. Infrastruct. Eng., vol. 33, no. 9, pp. 731-747, 2017.
[http://dx.doi.org/10.1111/mice.12334]
[13]
W. Choi, and Y.J. Cha, "SDDNet: Real-Time crack segmentation", IEEE Trans. Ind. Electron., vol. 67, no. 9, pp. 8016-8025, 2020.
[http://dx.doi.org/10.1109/TIE.2019.2945265]
[14]
M.V. Osch, D. Bera, K.V. Hee, Y. Koks, and H. Zeegers, "Tele-operated service robots: ROSE", Autom. Construct., vol. 39, pp. 152-160, 2014.
[http://dx.doi.org/10.1016/j.autcon.2013.06.009]
[15]
S.W. Yang, C.S. Lin, S.K. Lin, S.H. Fu, and M.S. Yeh, "An automatic optical inspection system for measuring a microlens array with an optical interferometric microscope and genetic algorithm", Assem. Autom., vol. 33, no. 1, pp. 57-67, 2013.
[http://dx.doi.org/10.1108/01445151311294720]
[16]
M. Nadeem, A. Hussain, A. Munir, M. Habib, and M.T. Naseem, "Removal of random valued impulse noise from grayscale images using quadrant based spatially adaptive fuzzy filter", Signal Processing, vol. 169, p. 107403, 2020.
[http://dx.doi.org/10.1016/j.sigpro.2019.107403]
[17]
C.H. Lee, L.H. Chen, and W.K. Wang, "Image contrast enhancement using classified virtual exposure image fusion", IEEE Trans. Consum. Electron., vol. 58, no. 4, pp. 1253-1261, 2012.
[http://dx.doi.org/10.1109/TCE.2012.6414993]
[18]
J. Shi, and Y. Cai, "A novel image enhancement method using local Gamma correction with three-level thresholding", In 6th IEEE Joint International Information Technology and Artificial Intelligence Conference. Vol. 1, pp. 374-378, Sept 2011.
[http://dx.doi.org/10.1109/ITAIC.2011.6030226]
[19]
L. Hu, and J. Cui, "Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm", Measurement, vol. 145, pp. 150-159, 2019.
[http://dx.doi.org/10.1016/j.measurement.2019.02.006]
[20]
Z.H. Zhu, S. German, and L. Brilakis, "Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation", Autom. Construct., vol. 20, no. 7, pp. 874-883, 2011.
[http://dx.doi.org/10.1016/j.autcon.2011.03.004]
[21]
S. Dorafshan, R. Thomas, and M. Maguire, "Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete", Constr. Build. Mater., vol. 186, pp. 1031-1045, 2018.
[http://dx.doi.org/10.1016/j.conbuildmat.2018.08.011]
[22]
L. Yu, C. Li, and S. Fei, "Any-wall touch control system with switching filter based on 3D sensor", IEEE Sens. J., vol. 18, no. 11, pp. 4697-4703, 2018.
[http://dx.doi.org/10.1109/JSEN.2018.2827386]
[23]
T.T. Yu, A.X. Zhu, and Y.Y. Chen, "Efficient crack detection method for tunnel lining surface cracks based on infrared images", J. Comput. Civ. Eng., vol. 31, no. 3, p. 04016067, 2017.
[http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000645]
[24]
B. H. Shan, S. J. Zheng, and J. P. Ou, "A stereovision-based crack width detection approach for concrete surface assessment", J. Civ. Eng. Vol. 20, No. 2, pp. 803-812, Apr 2015.
[http://dx.doi.org/10.1007/s12205-015-0461-6]

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