Open Access Open Access  Restricted Access Subscription or Fee Access

A Thermal Imaging Model for Roads Cracks Width Detection


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecap.v13i3.23001

Abstract


Many roads have a punch of defects that negatively affect driving safety. The most popular defects on the roads are the cracks which need to be detected and estimated to determine the proper required action in order to provide safer road conditions. This paper aims to provide an algorithm to improve the Inspection process of roads which has difficulties in how to detect cracks and estimate their widths and therefore evaluate their effects on roads. In this paper, a novel algorithm has been developed to detect cracks on the roads and to estimate their width. The developed algorithm is based on locating cracks via horizontal and vertical pixels scan and then estimating the width of the cracks based on the real dimensions of the tested area. A thermal imaging tool was used in order to get an appropriate inspection process, where the Canny Edge Detector is applied to detect the cracks accurately. Such a model would be used by authorities that care about roads and transportation. The results show a promising detection and estimation of road cracks in real measurements which gives an indication of the effect of the cracks on the whole of the tested area.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords


Cracks Detection; Cracks Width; Edge Detection; Thermal Inspection

Full Text:

PDF


References


Balaras, C. A., and A. A. Argiriou. Infrared thermography for building diagnostics, Energy and buildings, 34.2 (2002): 171-183.
https://doi.org/10.1016/S0378-7788(01)00105-0

Kaplan, Herbert. Practical applications of infrared thermal sensing and imaging equipment. Vol. 75. SPIE press, (2007).
https://doi.org/10.1117/3.725072

Changhang Xu, Jing Xie, Guoming Chen, Weiping Huang, An infrared thermal image processing framework based on superpixel algorithm to detect cracks on metal surface Infrared Phys., Technol., 67 (2014), pp. 266-272
https://doi.org/10.1016/j.infrared.2014.08.002

Yusuke Fujita, Yoshihiko Hamamoto, A robust automatic crack detection method from noisy concrete surfaces, Mach. Vis. Appl. 22 (2) (2011), pp. 245-254
https://doi.org/10.1007/s00138-009-0244-5

Will S.M. Brooks, Dan A. Lamb, Stuart J.C. Irvine, IR reflectance imaging for crystalline Si solar cell crack detection, IEEE J. Photovolt., 5 (5) (2015), pp. 1271-1275.
https://doi.org/10.1109/JPHOTOV.2015.2438636

Cuixiang Pei, Jinxin Qiu, Haocheng Liu, Zhenmao Chen, Simulation of surface cracks measurement in first walls by laser spot array thermography, Fusion Eng. Des. (2015).

M. Rodriguez-Martina, S. Laguelaa, D. Gonzalez-Aguilera, J. Martinez, Thermographic test for the geometric characterization of cracks in welding using IR image rectification, Autom. Constr., 61 (2016), pp. 58-65.
https://doi.org/10.1016/j.autcon.2015.10.012

J. Yang, W. Wang, G. Lin, Q. Li, Y. Sun and Y. Sun, Infrared Thermal Imaging-Based Crack Detection Using Deep Learning, in IEEE Access, vol. 7, pp. 182060-182077, 2019.
https://doi.org/10.1109/ACCESS.2019.2958264

C. Maierhofer and M. Rollig, Active thermography for the characterization of surfaces and interfaces of historic masonry structures, in Proc. 7th Int. Symp. Non-Destructive Test. Civil Eng. (NDTCE), Nantes, France, vol. 30, 2009, pp. 1-6.

S. Bagavathiappan, B. B. Lahiri, T. Saravanan, J. Philip, and T. Jayakumar, Infrared thermography for condition monitoring- A review, Infr. Phys. Technol., vol. 60, pp. 35-55, Sep. 2013.
https://doi.org/10.1016/j.infrared.2013.03.006

B. Lahiri, S. Bagavathiappan, L. T. Sebastian, J. Philip, and T. Jayakumar, Effect of non-magnetic inclusions in magnetic specimens on defect detection sensitivity using active infrared thermography, Infr. Phys. Technol., vol. 68, pp. 52-60, Jan. 2015.
https://doi.org/10.1016/j.infrared.2014.11.004

S. Dorafshan, M. Maguire, N. V. Hoffer, C. Coopmans, and R. J. Thomas, Unmanned aerial vehicle augmented bridge inspection feasibility study, Dept. Elect. Comput. Eng., Utah State Univ., Logan, UT, USA, Tech. Rep. Unmanned Aerial Vehicle Augmented Bridge Inspection Feasibility Study (No. CAIT-UTC-NC31), 2017.

E. Barreira and V. P. de Freitas, Evaluation of building materials using infrared thermography, Construct. Building Mater., vol. 21, no. 1, pp. 218-224, 2007.
https://doi.org/10.1016/j.conbuildmat.2005.06.049

N. Pech-May, A. Oleaga, A. Mendioroz, and A. Salazar, Fast characterization of the width of vertical cracks using pulsed laser spot infrared thermography, J. Nondestruct. Eval., vol. 35, no. 2, p. 22, 2016.
https://doi.org/10.1007/s10921-016-0344-x

T. Pahlberg, M. Thurley, D. Popovic, and O. Hagman, Crack detection in oak flooring lamellae using ultrasound excited thermography, Infr. Phys. Technol., vol. 88, pp. 57-69, Jan. 2018.
https://doi.org/10.1016/j.infrared.2017.11.007

S. Beuve, Z. Qin, J.-P. Roger, S. Hol'e, and C. Bou'e, Open cracks depth sizing by multi-frequency laser stimulated lock-in thermography combined with image processing, Sens. Actuators A, Phys., vol. 247, pp. 494-503, Aug. 2016.
https://doi.org/10.1016/j.sna.2016.06.028

X. Li, B. Gao, W. L. Woo, G. Y. Tian, X. Qiu, and L. Gu, Quantitative surface crack evaluation based on eddy current pulsed thermography, IEEE Sensors J., vol. 17, no. 2, pp. 412-421, Jan. 2017.
https://doi.org/10.1109/JSEN.2016.2625815

R. Ali and Y.-J. Cha, Subsurface damage detection of a steel bridge using deep learning and uncooled micro bolometer, Construct. Building Mater., vol. 226, pp. 376-387, Nov. 2019.
https://doi.org/10.1016/j.conbuildmat.2019.07.293

Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol. 521, no. 7553, p. 436, 2015.
https://doi.org/10.1038/nature14539

D. Kang and Y.-J. Cha, Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging, Comput. Aided Civil Infrastruct. Eng., vol. 33, no. 10, pp. 885-902, Oct. 2018.
https://doi.org/10.1111/mice.12375

D. Soukup and R. Huber-M¨ork, Convolutional neural networks for steel surface defect detection from photometric stereo images, in Proc. Int. Symp. Vis. Comput. Cham, Switzerland: Springer, 2014, pp. 668-677.
https://doi.org/10.1007/978-3-319-14249-4_64

Y.-J. Cha, W. Choi, G. Suh, S. Mahmoudkhani, and O. B¨uy¨uk¨ozt¨urk, Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types, Comput.-Aided Civil Infrastruct. Eng., vol. 33, no. 9, pp. 731-747, 2018.
https://doi.org/10.1111/mice.12334

T. Kong, A. Yao, Y. Chen, and F. Sun, HyperNet: Towards accurate region proposal generation and joint object detection, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2016, pp. 845-853.
https://doi.org/10.1109/CVPR.2016.98

Alfaqs, F., Gaith, M., Haddadin, Z., Al-Adwan, I., Damage Detection in Cantilever Piping System-Transporting Fluid Using Finite Element Method, (2022) International Review of Mechanical Engineering (IREME), 16 (10), pp. 548-554.
https://doi.org/10.15866/ireme.v16i10.22319

Boudi, K., Abdelaziz, Y., Benkheira, S., Abderrahmani, R., Crack-Tip Singularity Modelling by IQPE Element, (2020) New Trends in Nonlinear Analysis and Applications, 2 (1), pp. 21-26.

Ghintab, S., Hassan, M., Localization for Autonomous Vehicles Based on Deep Learning Network, (2023) International Review of Electrical Engineering (IREE), 18 (2), pp. 128-135.
https://doi.org/10.15866/iree.v18i2.22581

Athamneh, A., Alqudah, A., Aqeil, R., Line Voltage Based Distance Relay Using a Multistage Convolutional Neural Network Classifier, (2021) International Review of Electrical Engineering (IREE), 16 (6), pp. 553-565.
https://doi.org/10.15866/iree.v16i6.20962

Al-Zabt, A., Tutunji, T., Robotic Manipulator Control Using CNN and Deep Q-Network Algorithm, (2022) International Review of Automatic Control (IREACO), 15 (5), pp. 242-250.
https://doi.org/10.15866/ireaco.v15i5.19762

Bal-Ghaoui, M., El Yousfi Alaoui, M., Jilbab, A., Bourouhou, A., Enhanced Ultrasound Breast Cancer Classification Based on Sparse Data and Two Customized Deep Learning Approaches, (2022) International Review on Modelling and Simulations (IREMOS), 15 (3), pp. 146-153.
https://doi.org/10.15866/iremos.v15i3.21504


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize