A Comparative Review of Image Processing Based Crack Detection Techniques on Civil Engineering Structures

Document Type : Regular Article

Authors

1 Department of Information and Communication Technology, Bangladesh University of Professionals (BUP), Dhaka, Bangladesh

2 Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chattogram, Bangladesh

Abstract

Crack detection and repair of the cracks in engineering structures is essential to ensure serviceability and durability. Traditionally, cracks are detected by the examiner's visual inspection; as a result, crack detection and estimation of characteristics are greatly dependent on the examiner's personal judgment, which has aided in the repair of various structures and evaluation of the crack phenomenon in previous decades. Due to industrial advancement, the number of engineering structures has increased, but compared to that, expertise in the crack detection field did not raise that level. So, a less time-consuming and more accurate approach is needed. The image processing technique works simultaneously to detect the cracks with their attributes. In this context, the development of the algorithm and the implementation procedure is also simple. But some defects such as identifying noises as cracks and weakness in identifying micro-cracks have become significant challenges for this technique. Unable to locate transverse cracks in concrete structures is also a vital issue. So, to develop an accurate method, an extensive survey on the current articles is needed. In this paper, a critical analysis has been done on crack detection through the image processing phenomenon and a detailed literature review to understand the prospects of this method. From the literature review, it was observed that a general structure of CNN-based algorithm with camera images for crack detection could be an efficient approach with higher accuracy.

Keywords

Main Subjects

References

[1]     Budiansky B, O’connell RJ. Elastic moduli of a cracked solid. Int J Solids Struct 1976;12:81–97. doi:10.1016/0020-7683(76)90044-5.
[2]     Torok MM, Golparvar-Fard M, Kochersberger KB. Image-Based Automated 3D Crack Detection for Post-disaster Building Assessment. J Comput Civ Eng 2014;28. doi:10.1061/(ASCE)CP.1943-5487.0000334.
[3]     Jahangir H, Esfahani MR. Structural Damage Identification Based on Modal Data and Wavelet Analysis. 3rd Natl. Conf. Earthq. Struct., 2012.
[4]     Kim H, Ahn E, Cho S, Shin M, Sim S-H. Comparative analysis of image binarization methods for crack identification in concrete structures. Cem Concr Res 2017;99:53–61. doi:10.1016/j.cemconres.2017.04.018.
[5]     Oliveira H, Correia PL. Automatic Road Crack Detection and Characterization. IEEE Trans Intell Transp Syst 2013;14:155–68. doi:10.1109/TITS.2012.2208630.
[6]     Fujita Y, Hamamoto Y. A robust automatic crack detection method from noisy concrete surfaces. Mach Vis Appl 2011;22:245–54. doi:10.1007/s00138-009-0244-5.
[7]     Garber D, Shahrokhinasab E. Performance Comparison of In-Service, Full-Depth Precast Concrete Deck Panels to Cast-in-Place Decks. Accelerated Bridge Construction University Transportation Center (ABC-UTC); 2019.
[8]     Niemeier W, Riedel B, Fraser C, Neuss H, Stratmann R, Ziem E. New digital crack monitoring system for measuring and documentation of width of cracks in concrete structures. Proc. 13th FIG Symp. Deform. Meas. Anal. 14th IAG Symp. Geod. Geotech. Struct. Eng. Lisbon, 2008, p. 12–5.
[9]     Mohan A, Poobal S. Crack detection using image processing: A critical review and analysis. Alexandria Eng J 2018;57:787–98. doi:10.1016/j.aej.2017.01.020.
[10]   Geethalakshmi SN. A survey on crack detection using image processing techniques and deep learning algorithms. Int J Pure Appl Math 2018;118:215–20.
[11]    Liu Z, Cao Y, Wang Y, Wang W. Computer vision-based concrete crack detection using U-net fully convolutional networks. Autom Constr 2019;104:129–39. doi:10.1016/j.autcon.2019.04.005.
[12]   Ren Y, Huang J, Hong Z, Lu W, Yin J, Zou L, et al. Image-based concrete crack detection in tunnels using deep fully convolutional networks. Constr Build Mater 2020;234:117367. doi:10.1016/j.conbuildmat.2019.117367.
[13]   Ito A, Aoki Y, Hashimoto S. Accurate extraction and measurement of fine cracks from concrete block surface image. IEEE 2002 28th Annu. Conf. Ind. Electron. Soc. IECON 02, vol. 3, IEEE; n.d., p. 2202–7. doi:10.1109/IECON.2002.1185314.
[14]   Bhat S, Naik S, Gaonkar M, Sawant P, Aswale S, Shetgaonkar P. A Survey On Road Crack Detection Techniques. 2020 Int. Conf. Emerg. Trends Inf. Technol. Eng., IEEE; 2020, p. 1–6. doi:10.1109/ic-ETITE47903.2020.67.
[15]   Yao Y, Tung S-TE, Glisic B. Crack detection and characterization techniques-An overview. Struct Control Heal Monit 2014;21:1387–413. doi:10.1002/stc.1655.
[16]   Anitha MJ, Hemalatha R, Radha S. A Survey on Crack Detection Algorithms for Concrete Structures, 2021, p. 639–54. doi:10.1007/978-981-15-5029-4_53.
[17]   Dung CV, Anh LD. Autonomous concrete crack detection using deep fully convolutional neural network. Autom Constr 2019;99:52–8. doi:10.1016/j.autcon.2018.11.028.
[18]   Park SE, Eem S-H, Jeon H. Concrete crack detection and quantification using deep learning and structured light. Constr Build Mater 2020;252:119096. doi:10.1016/j.conbuildmat.2020.119096.
[19]   Noh Y, Koo D, Kang Y-M, Park D, Lee D. Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering. 2017 Int. Conf. Appl. Syst. Innov., IEEE; 2017, p. 877–80. doi:10.1109/ICASI.2017.7988574.
[20]   Vora J, Patel M, Tanwar S, Tyagi S. Image Processing Based Analysis of Cracks on Vertical Walls. 2018 3rd Int. Conf. Internet Things Smart Innov. Usages, IEEE; 2018, p. 1–5. doi:10.1109/IoT-SIU.2018.8519926.
[21]   Su C, Wang W. Concrete Cracks Detection Using Convolutional NeuralNetwork Based on Transfer Learning. Math Probl Eng 2020;2020:1–10. doi:10.1155/2020/7240129.
[22]   Kong X, Zhang Z, Meng L, Tomiyama H. Machine Learning Based Features Matching for Fatigue Crack Detection. Procedia Comput Sci 2020;174:101–5. doi:10.1016/j.procs.2020.06.063.
[23]   Flah M, Suleiman AR, Nehdi ML. Classification and quantification of cracks in concrete structures using deep learning image-based techniques. Cem Concr Compos 2020;114:103781. doi:10.1016/j.cemconcomp.2020.103781.
[24]   Yamane T, Chun P. Crack Detection from a Concrete Surface Image Based on Semantic Segmentation Using Deep Learning. J Adv Concr Technol 2020;18:493–504. doi:10.3151/jact.18.493.
[25]   Silva WRL da, Lucena DS de. Concrete Cracks Detection Based on Deep Learning Image Classification. Proceedings 2018;2:489. doi:10.3390/ICEM18-05387.
[26]   Zhang L, Yang F, Daniel Zhang Y, Zhu YJ. Road crack detection using deep convolutional neural network. 2016 IEEE Int. Conf. Image Process., IEEE; 2016, p. 3708–12. doi:10.1109/ICIP.2016.7533052.
[27]   Liu X, Ai Y, Scherer S. Robust image-based crack detection in concrete structure using multi-scale enhancement and visual features. 2017 IEEE Int. Conf. Image Process., IEEE; 2017, p. 2304–8. doi:10.1109/ICIP.2017.8296693.
[28]   Mei Q, Gül M, Azim MR. Densely connected deep neural network considering connectivity of pixels for automatic crack detection. Autom Constr 2020;110:103018. doi:10.1016/j.autcon.2019.103018.
[29]   Shao C, Chen Y, Xu F, Wang S. A Kind of Pavement Crack Detection Method Based on Digital Image Processing. 2019 IEEE 4th Adv. Inf. Technol. Electron. Autom. Control Conf., IEEE; 2019, p. 397–401. doi:10.1109/IAEAC47372.2019.8997810.
[30]   Akagic A, Buza E, Omanovic S, Karabegovic A. Pavement crack detection using Otsu thresholding for image segmentation. 2018 41st Int. Conv. Inf. Commun. Technol. Electron. Microelectron., IEEE; 2018, p. 1092–7. doi:10.23919/MIPRO.2018.8400199.
[31]   Fei Y, Wang KCP, Zhang A, Chen C, Li JQ, Liu Y, et al. Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V. IEEE Trans Intell Transp Syst 2020;21:273–84. doi:10.1109/TITS.2019.2891167.
[32]   Yusof NAM, Osman MK, Noor MHM, Ibrahim A, Tahir NM, Yusof NM. Crack Detection and Classification in Asphalt Pavement Images using Deep Convolution Neural Network. 2018 8th IEEE Int. Conf. Control Syst. Comput. Eng., IEEE; 2018, p. 227–32. doi:10.1109/ICCSCE.2018.8685007.
[33]   Nie M, Wang C. Pavement Crack Detection based on yolo v3. 2019 2nd Int. Conf. Saf. Prod. Informatiz., IEEE; 2019, p. 327–30. doi:10.1109/IICSPI48186.2019.9095956.
[34]   Buza E, Akagic A, Besic I. Image-Based Crack Detection Using Sub-image Technique. 2019 11th Int. Conf. Electr. Electron. Eng., IEEE; 2019, p. 614–8. doi:10.23919/ELECO47770.2019.8990615.
[35]   Kang S-M, Chun C-J, Shim S-B, Ryu S-K, Baek J-D. Real Time Image Processing System for Detecting Infrastructure Damage: Crack. 2019 IEEE Int. Conf. Consum. Electron., IEEE; 2019, p. 1–3. doi:10.1109/ICCE.2019.8661830.
[36]   Qin J, Zhu Q, Li L, Dong L. Pavement Cracks Automated Processing Based On Image Detection with Neutral Network. 2020 12th Int. Conf. Intell. Human-Machine Syst. Cybern., IEEE; 2020, p. 103–6. doi:10.1109/IHMSC49165.2020.00031.
[37]   Wu G, Sun X, Zhou L, Zhang H, Pu J. Research on crack detection algorithm of asphalt pavement. 2015 IEEE Int. Conf. Inf. Autom., IEEE; 2015, p. 647–52. doi:10.1109/ICInfA.2015.7279366.
[38]   Jo J, Jadidi Z. A high precision crack classification system using multi-layered image processing and deep belief learning. Struct Infrastruct Eng 2020;16:297–305. doi:10.1080/15732479.2019.1655068.
[39]   Doulamis A, Doulamis N, Protopapadakis E, Voulodimos A. Combined Convolutional Neural Networks and Fuzzy Spectral Clustering for Real Time Crack Detection in Tunnels. 2018 25th IEEE Int. Conf. Image Process., IEEE; 2018, p. 4153–7. doi:10.1109/ICIP.2018.8451758.
[40]   Niu B, Wu H, Meng Y. Application of CEM Algorithm in the Field of Tunnel Crack Identification. 2020 IEEE 5th Int. Conf. Image, Vis. Comput., IEEE; 2020, p. 232–6.
[41]   Wang Y, Zhang JY, Liu JX, Zhang Y, Chen ZP, Li CG, et al. Research on Crack Detection Algorithm of the Concrete Bridge Based on Image Processing. Procedia Comput Sci 2019;154:610–6. doi:10.1016/j.procs.2019.06.096.
[42]   Yu Z, Shen Y, Shen C. A real-time detection approach for bridge cracks based on YOLOv4-FPM. Autom Constr 2021;122:103514. doi:10.1016/j.autcon.2020.103514.
[43]   Chaudhari CV, Gupta RK, Feagade SA. A Novel Approach of Crack Detection in Railway Track using Fuzzy C Means and Level Set Method. 2nd Int. Conf. Data, Eng. Appl., IEEE; 2020, p. 1–7. doi:10.1109/IDEA49133.2020.9170669.
[44]   Sambo B, Bevan A, Pislaru C. A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model. Int. Conf. Railw. Eng. (ICRE 2016), Institution of Engineering and Technology; 2016, p. 12 (9 .)-12 (9 .). doi:10.1049/cp.2016.0521.
[45]   Yin L, Wu J, Ye B, Avila JS, Xu H, How KY, et al. Imaging and detection of cracks based on a multi-frequency electromagnetic scanning instrument and SVM. 2017 IEEE Int. Conf. Imaging Syst. Tech., IEEE; 2017, p. 1–5. doi:10.1109/IST.2017.8261512.

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  • Receive Date: 24 May 2021
  • Revise Date: 11 August 2021
  • Accept Date: 14 September 2021

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