Toward High-Precision Crack Detection in Concrete Bridges Using Deep Learning
Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 3
Abstract
Concrete cracks that cause durability problems and bearing capacity failure are critical for evaluating the in-service performance of concrete bridges. It is still challenging for deep-learning methods to accurately detect and quantify concrete cracks. To improve detection precision, a computer-vision (CV)-based crack detection framework is presented in this study. The proposed framework introduces redesigned deep convolutional generative adversarial networks (DCGANs) and extends the dataset of concrete crack images by generating synthetic examples from collected crack images. Based on the enlarged dataset and the you-only-look-once version 5s (YOLOv5s) algorithm, model training and crack detection are conducted through backbone, bottleneck, and prediction part. Subsequently, two algorithms, including the Ostu method and the medial axis algorithm, are combined to calculate the length and width of concrete cracks. Experimental tests compared the YOLOv5s model with YOLO series algorithms, region-based fast convolutional neural network (faster R-CNN), and single shot multibox detector (SSD). The dataset augmentation by redesigned DCGANs increased mean average precision (mAP) by 3.7%, and YOLOv5s outperformed in detection speed with . The crack size measurement of concrete members in the laboratory demonstrates that the calculation of crack size is achieved at pixel level. The proposed concrete crack detection framework can meet precision requirements and provide a promising measure for patrol inspection.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors greatly acknowledge the financial support from the National Natural Science Foundation of China (51978155 and 52108274) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. SJCX21_0056). The authors also thank Postdoctoral Fellow Yiming Zhang of The Hong Kong Polytechnic University, doctoral candidate Hui Gao, and doctoral candidate Ruijun Liang of Southeast University for paper writing and computing. Finally, contributions by the anonymous reviewers are also highly appreciated.
References
Abdel-Qader, I., O. Abudayyeh, and M. E. Kelly. 2003. “Analysis of edge-detection techniques for crack identification in bridges.” J. Comput. Civ. Eng. 17 (4): 255–263. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255).
ACI (American Concrete Institute). 2008. Building code requirements for structural concrete and commentary. ACI 318-08/ACI 318R-08. Farmington Hills, MI: ACI.
Avci, O., O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj, and D. J. Inman. 2021. “A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications.” Mech. Syst. Signal Process 147 (Jan): 107077. https://doi.org/10.1016/j.ymssp.2020.107077.
Bochkovskiy, A., C. Y. Wang, and H. M. Liao. 2020. “Yolov4: Optimal speed and accuracy of object detection.” Preprint, submitted April 23, 2020. https://arxiv.org/abs/2004.10934.
Cao, G., Y. Zhao, R. Ni, L. Yu, and H. Tian. 2010. “Forensic detection of median filtering in digital images.” In Proc., IEEE Int. Conf. on Multimedia and Expo, 89–94. New York: IEEE. https://doi.org/10.1109/ICME.2010.5583869.
Cha, Y. J., W. Choi, and O. Büyüköztürk. 2017. “Deep learning-based crack damage detection using convolutional neural networks.” Comput.-Aided Civ. Infrastruct. Eng. 32 (5): 361–378. https://doi.org/10.1111/mice.12263.
Cha, Y. J., W. Choi, G. Suh, S. Mahmoudkhani, and O. Büyüköztürk. 2018. “Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types.” Comput.-Aided Civ. Infrastruct. Eng. 33 (9): 731–747. https://doi.org/10.1111/mice.12334.
Cosenza, E., and D. Losanno. 2021. “Assessment of existing reinforced-concrete bridges under road-traffic loads according to the new Italian guidelines.” Struct. Concr. 22 (5): 2868–2881. https://doi.org/10.1002/suco.202100147.
Deng, J., Y. Lu, and V. C. Lee. 2020. “Concrete crack detection with handwriting script interferences using faster region-based convolutional neural network.” Comput.-Aided Civ. Infrastruct. Eng. 35 (4): 373–388. https://doi.org/10.1111/mice.12497.
Deng, J., Y. Lu, and V. C. Lee. 2021. “Imaging-based crack detection on concrete surfaces using you only look once network.” Struct. Health Monit. 20 (2): 484–499. https://doi.org/10.1177/1475921720938486.
Dorafshan, S., R. J. Thomas, and M. Maguire. 2018. “SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks.” Data Brief 21 (Dec): 1664–1668. https://doi.org/10.1016/j.dib.2018.11.015.
Du, Y., N. Pan, Z. Xu, F. Deng, Y. Shen, and H. Kang. 2021. “Pavement distress detection and classification based on YOLO network.” Int. J. Pavement Eng. 22 (13): 1659–1672. https://doi.org/10.1080/10298436.2020.1714047.
Dung, C. V. 2019. “Autonomous concrete crack detection using deep fully convolutional neural network.” Autom. Constr. 99 (Mar): 52–58. https://doi.org/10.1016/j.autcon.2018.11.028.
Galteri, L., L. Seidenari, M. Bertini, and A. Del Bimbo. 2019. “Towards real-time image enhancement GANs.” In Proc., 2019 Int. Conf. on Computer Analysis of Images and Patterns, 183–195. Cham, Switzerland: Springer.
Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deeplearning. Cambridge, MA: MIT Press.
Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. “Generative adversarial nets.” In Advances in neural information processing systems, 27. Red Hook, NY: Curran Associates, Inc.
Hoskere, V., Y. Narazaki, T. A. Hoang, and B. Spencer, Jr. 2020. “MaDnet: Multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure.” J. Civ. Struct. Health Monit. 10 (5): 757–773. https://doi.org/10.1007/s13349-020-00409-0.
Hoskere, V., Y. Narazaki, and B. F. Spencer. 2022. “Physics–Based graphics models in 3D synthetic environments as autonomous vision–Based inspection testbeds.” Sensors (Basel) 22 (2): 532. https://doi.org/10.3390/s22020532.
Jeong, E., J. Seo, and J. Wacker. 2020. “Literature review and technical survey on bridge inspection using unmanned aerial vehicles.” J. Perform. Constr. Facil. 34 (6): 04020113. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001519.
Jocher, G., K. Nishimura, and T. Mineeva. 2020. “YOLOv5[DB/OL].” Accessed June 26, 2020. https://github.com/ultralytics/yolov5.
Kang, D., S. S. Benipal, D. L. Gopal, and Y. J. Cha. 2020. “Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning.” Autom. Constr. 118 (Oct): 103291. https://doi.org/10.1016/j.autcon.2020.103291.
Lattanzi, D., and G. R. Miller. 2014. “Robust automated concrete damage detection algorithms for field applications.” J. Comput. Civ. Eng. 28 (2): 253–262. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000257.
Lee, S., N. Kalos, and D. H. Shin. 2014. “Non-destructive testing methods in the US for bridge inspection and maintenance.” KSCE J. Civ. Eng. 18 (5): 1322–1331. https://doi.org/10.1007/s12205-014-0633-9.
Lei, B., Y. Ren, N. Wang, L. Huo, and G. Song. 2020. “Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method.” Struct. Health Monit. 19 (6): 1871–1883. https://doi.org/10.1177/1475921719898862.
Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg. 2016. “Ssd: Single shot multibox detector.” In Proc., 2016 European Conf. on Computer Vision, 21–37. Cham, Switzerland: Springer.
Lucic, M., K. Kurach, M. Michalski, S. Gelly, and O. Bousquet. 2017. “Are gans created equal? A large-scale study.” Preprint, submitted November 28, 2017. https://arxiv.org/abs/1711.10337.
Mao, J., H. Wang, and J. Li. 2019. “Fatigue reliability assessment of a long-span cable-stayed bridge based on one-year monitoring strain data.” J. Bridge Eng. 24 (1): 05018015. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001337.
Miluccio, G., D. Losanno, F. Parisi, and E. Cosenza. 2021. “Traffic-load fragility models for prestressed concrete girder decks of existing Italian highway bridges.” Eng. Struct. 249 (Dec): 113367. https://doi.org/10.1016/j.engstruct.2021.113367.
Narazaki, Y., V. Hoskere, T. A. Hoang, Y. Fujino, A. Sakurai, and B. F. Spencer, Jr. 2020. “Vision-based automated bridge component recognition with high-level scene consistency.” Comput.-Aided Civ. Infrastruct. Eng. 35 (5): 465–482. https://doi.org/10.1111/mice.12505.
Nishikawa, T., J. Yoshida, T. Sugiyama, and Y. Fujino. 2012. “Concrete crack detection by multiple sequential image filtering.” Comput.-Aided Civ. Infrastruct. Eng. 27 (1): 29–47. https://doi.org/10.1111/j.1467-8667.2011.00716.x.
Opiyo, S., C. Okinda, J. Zhou, E. Mwangi, and N. Makange. 2021. “Medial axis-based machine-vision system for orchard robot navigation.” Comput. Electron. Agric. 185 (Jun): 106153. https://doi.org/10.1016/j.compag.2021.106153.
Ostu, N. 1979. “A threshold selection method from gray-level histogram.” IEEE Trans. SMC9 (1): 62–66. https://doi.org/10.1109/TSMC.1979.4310076.
Park, S. E., S. H. Eem, and H. Jeon. 2020. “Concrete crack detection and quantification using deep learning and structured light.” Constr. Build. Mater. 252 (Aug): 119096. https://doi.org/10.1016/j.conbuildmat.2020.119096.
Qi, S., Y. Dong, and Q. Mao. 2020. “Improving single shot detector for industrial cracks by feature resolution analysis.” In Proc., 3rd Int. Conf. on Artificial Intelligence and Pattern Recognition, 196–200. New York: IEEE. https://doi.org/10.1145/3430199.3430204.
Qi, Y., Z. Yang, W. Sun, M. Lou, J. Lian, W. Zhao, X. Deng, and Y. Ma. 2021. “A comprehensive overview of image enhancement techniques.” Arch. Comput. Methods Eng. 29: 583–607. https://doi.org/10.1007/s11831-021-09587-6.
Radford, A., L. Metz, and S. Chintala. 2015. “Unsupervised representation learning with deep convolutional generative adversarial networks.” Preprint, submitted November 19, 2015. https://arxiv.org/abs/1511.06434.
Redmon, J., S. Divvala, R. Girshick, and A. Farhadi. 2016. “You only look once: Unified, real-time object detection.” In Proc., 2016 IEEE Conf. on Computer Vision and Pattern Recognition, 779–788. New York: IEEE.
Redmon, J., and A. Farhadi. 2018. “Yolov3: An incremental improvement.” Preprint, submitted April 8, 2018. https://arxiv.org/abs/1804.02767.
Ren, S., K. He, R. Girshick, and J. Sun. 2015. “Faster R-CNN: Towards real-time object detection with region proposal networks.” IEEE Trans. Pattern Anal. Mach. Intell. 39 (6): 1137–1149. https://doi.org/ 10.1109/TPAMI.2016.2577031.
Rezatofighi, H., N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese. 2019. “Generalized intersection over union: A metric and a loss for bounding box regression.” In Proc., IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 658–666. New York: IEEE. https://doi.org/10.48550/arXiv.1902.09630.
Sacks, R., A. Kedar, A. Borrmann, L. Ma, I. Brilakis, P. Hüthwohl, S. Daum, U. Kattel, R. Yosef, and T. Liebich. 2018. “SeeBridge as next generation bridge inspection: Overview, information delivery manual and model view definition.” Autom. Constr. 90 (Jun): 134–145. https://doi.org/10.1016/j.autcon.2018.02.033.
Sinha, S. K., and P. W. Fieguth. 2006. “Automated detection of cracks in buried concrete pipe images.” Autom. Constr. 15 (1): 58–72. https://doi.org/10.1016/j.autcon.2005.02.006.
Spencer, B. F., Jr., V. Hoskere, and Y. Narazaki. 2019. “Advances in computer vision-based civil infrastructure inspection and monitoring.” Engineering 5 (2): 199–222. https://doi.org/10.1016/j.eng.2018.11.030.
Subirats, P., J. Dumoulin, V. Legeay, and D. Barba. 2006. “Automation of pavement surface crack detection using the continuous wavelet transform.” In Proc., 2006 Int. Conf. on Image Processing, 3037–3040. New York: IEEE.
Sun, L., Z. Shang, Y. Xia, S. Bhowmick, and S. Nagarajaiah. 2020. “Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection.” J. Struct. Eng. 146 (5): 04020073. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002535.
Sun, Z., D. M. Siringoringo, and Y. Fujino. 2021. “Load-carrying capacity evaluation of girder bridge using moving vehicle.” Eng. Struct. 229 (Feb): 111645. https://doi.org/10.1016/j.engstruct.2020.111645.
Vidanpathirana, M., I. Sudasingha, J. Vidanapathirana, P. Kanchana, and I. Perera. 2020. “Tracking and frame-rate enhancement for real-time 2D human pose estimation.” Vis. Comput. 36 (7): 1501–1519. https://doi.org/10.1007/s00371-019-01757-9.
Wang, W., W. Hu, W. Wang, X. Xu, M. Wang, Y. Shi, S. Qiu, and E. Tutumluer. 2021. “Automated crack severity level detection and classification for ballastless track slab using deep convolutional neural network.” Autom. Constr. 124 (Apr): 103484. https://doi.org/10.1016/j.autcon.2020.103484.
Weinstein, J. C., M. Sanayei, and B. R. Brenner. 2018. “Bridge damage identification using artificial neural networks.” J. Bridge Eng. 23 (11): 04018084. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001302.
Xiao, Y., Z. Tian, J. Yu, Y. Zhang, S. Liu, S. Du, and X. Lan. 2020. “A review of object detection based on deep learning.” Multimed. Tools. Appl. 79 (33): 23729–23791. https://doi.org/10.1007/s11042-020-08976-6.
Yamaguchi, T., and S. Hashimoto. 2010. “Fast crack detection method for large-size concrete surface images using percolation-based image processing.” Mach. Vis. Appl. 21 (5): 797–809. https://doi.org/10.1007/s00138-009-0189-8.
Yeum, C. M., and S. J. Dyke. 2015. “Vision-based automated crack detection for bridge inspection.” Comput.-Aided Civ. Infrastruct. Eng. 30 (10): 759–770. https://doi.org/10.1111/mice.12141.
Zhang, L. P., L. F. Zhang, and B. Du. 2016. “Deep learning for remote sensing data: A technical tutorial on the state of the art.” IEEE Geosci. Remote Sens. Mag. 4 (2): 22–40. https://doi.org/10.1109/MGRS.2016.2540798.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 37 • Issue 3 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 8, 2022
Accepted: Jan 20, 2023
Published online: Mar 25, 2023
Published in print: Jun 1, 2023
Discussion open until: Aug 25, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Mengyi Wang, Yu Zhou, Autonomous Rail Surface Defect Identification Based on an Improved One-Stage Object Detection Algorithm, Journal of Performance of Constructed Facilities, 10.1061/JPCFEV.CFENG-4816, 38, 5, (2024).