Skip to main content
SpringerLink
Log in

Automated corrosion detection using deep learning and computer vision

  • Research
  • Published:
Asian Journal of Civil Engineering Aims and scope Submit manuscript

Abstract

Over the past few decades, there has been an increase in the number of aging civil structures worldwide, most of which are made of concrete. Concrete may lose strength as a result of continuous loading and environmental factors. It has been challenging to detect corrosion damage in industrial and civil constructions, and the existing structures must be maintained manually, which has proven to be subjective and unreliable. Thus, it is essential to have a system for detecting defects in concrete structures, such as seeing an automatic corrosion product which could early show the warning of damages to avoid disasters caused by structural failures. This research used three versions of the You Only Look Once (YOLO) object detection technique to identify concrete corrosion from real-world images. The training was conducted using concrete corrosion images for YOLOv3, YOLOv5s, and YOLOv7 models. The performance of YOLOv3, YOLOv5s, and YOLOv7 was compared using evaluation metrics such as accuracy, F1 score, recall, and mean average precision (mAP). The results showed that YOLOv3, YOLOv5s, and YOLOv7 had F1 scores of 0.85, 0.83, and 0.80, respectively. Among all these models, YOLOv5s achieved the highest mAP@0.5 values of 0.88 which outperforms other state-of-the-art detectors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The data that support the findings of this study are openly available at https://universe.roboflow.com/photolab/bgc-1.

References

  • Ali, L., Alnajjar, F., Parambil, M. M. A., Younes, M. I., Abdelhalim, Z. I., & Aljassmi, H. (2022). Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions. https://doi.org/10.3390/s22228820

  • Cha, Y. J., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018). Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering, 33(9), 731–747. https://doi.org/10.1111/mice.12334

    Article  Google Scholar 

  • Jocher, G., Chaurasia, A., Stoken, A., & Borovec, J. (2022). Ultralytics/yolov5: v7.0—YOLOv5 SOTA realtime instance segmentation. Accessed March 22, 2022. https://zenodo.org/record/7347926#.ZEZ8_85ByUk

  • Kaveh, A., & Iranmanesh, A. (1998). Comparative study of backpropagation and improved counterpropagation neural nets in structural analysis and optimization. International Journal of Space Structures, 13(4), 177–185. https://doi.org/10.1177/026635119801300401

    Article  Google Scholar 

  • Kaveh, A., & Khalegi, A. (1998). Prediction of strength for concrete specimens using artificial neural networks. In: B. Topping, (ed.) (pp. 165–171). Edinburgh CL, Edinburgh, Scotland: Civil Comp Press.

  • Kaveh, A., & Khavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. Structures, 52, 256–272. https://doi.org/10.1016/j.istruc.2023.03.178

    Article  Google Scholar 

  • Kaveh, A., & Servati, H. (2001). Design of double layer grids using backpropagation neural networks. Computers and Structures, 79(17), 1561–1568. https://doi.org/10.1016/S0045-7949(01)00034-7

    Article  Google Scholar 

  • Kaveh, A., Gholipour, Y., & Rahami, H. (2008). Optimal design of transmission towers using genetic algorithm and neural networks. International Journal of Space Structures, 23, 1–19. https://doi.org/10.1260/026635108785342073

    Article  Google Scholar 

  • Lee, B.-Y., Kim, Y.-Y., & Kim, J.-K. (2005). Development of image processing for concrete surface cracks by employing enhanced binarization and shape analysis technique. Journal of the Korea Concrete Institute, 17, 361–368.

    Article  Google Scholar 

  • Li, S., & Zhao, X. (2019). Image-based concrete crack detection using convolutional neural network and exhaustive search technique. Advances in Civil Engineering. https://doi.org/10.1155/2019/6520620

    Article  Google Scholar 

  • Li, G., Zhao, X., Du, K., Ru, F., & Zhang, Y. (2017). Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine. Automation in Construction, 78, 51–61. https://doi.org/10.1016/j.autcon.2017.01.019

    Article  Google Scholar 

  • Liu, Y., Ji, X., Pei, S., Zhang, G., Lin, Y., & Chen, Y. (2020). Research on automatic location and recognition of insulators in substation based on YOLOv3. High Voltage, 5, 62–68. https://doi.org/10.1049/hve.2019.0091

    Article  Google Scholar 

  • Liu, Z., Suandi, S. A., Ohashi, T., & Ejima, T. (2002). A tunnel crack detection and classification systems based. Machine Vision Applications in Industrial Inspection X, 4664, 145–152.

    Article  Google Scholar 

  • Na, W., & Tao, W. (2012). Proximal support vector machine based pavement image classification. In 2012 IEEE 5th Int. Conf. Adv. Comput. Intell. ICACI 2012 (c) (pp. 686–688). https://doi.org/10.1109/ICACI.2012.6463255

  • Payab, M., & Khanzadi, M. (2020). State of the art and a new methodology based on multi-agent fuzzy system for concrete crack detection and type classification. Archives of Computational Methods in Engineering, 28, 2509–2542.

    Article  Google Scholar 

  • Photolab. (2022). BGC-1 dataset. Roboflow Universe.

    Google Scholar 

  • Prasanna, P., Dana, K. J., Gucunski, N., Basily, B. B., La, H. M., Lim, R. S., & Parvardeh, H. (2016). Automated crack detection on concrete bridges. IEEE Transactions on Automation Science and Engineering, 13(2), 591–599. https://doi.org/10.1109/TASE.2014.2354314

    Article  Google Scholar 

  • Redmon, J., Santosh Divvala, R. G., & Farhadi, A. (2016).“You only look once: Unified, real-time object detection. In Proc. IEEE Conf. Comput. Vis. pattern Recognit. (pp. 779–788).

  • Taffese, W. Z., & Nigussie, E. (2020). Autonomous corrosion assessment of reinforced concrete structures: Feasibility study. Sensors, 20, 6825.

    Article  Google Scholar 

  • Tong, Z., Gao, J., Han, Z., & Wang, Z. (2018). Recognition of asphalt pavement crack length using deep convolutional neural networks. Road Materials and Pavement Design, 19(6), 1334–1349. https://doi.org/10.1080/14680629.2017.1308265

    Article  Google Scholar 

  • Wu, D., Jiang, S., Zhao, E., Liu, Y., Zhu, H., Wang, W., & Wang, R. (2022). Detection of Camellia oleifera fruit in complex scenes by using YOLOv7 and data augmentation. Applied Sciences, 12, 11318.

    Article  Google Scholar 

  • Zhang, A., Wang, K. C. P., Fei, Y., Liu, Y., Chen, C., Yang, G., Li, J. Q., Yang, E., & Qiu, S. (2019). Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network. Computer-Aided Civil and Infrastructure Engineering, 34(3), 213–229. https://doi.org/10.1111/mice.12409

    Article  Google Scholar 

  • Zhang, K., Zhang, Y., & Cheng, H. D. (2020). Self-supervised structure learning for crack detection based on cycle-consistent generative adversarial networks. Journal of Computing in Civil Engineering, 34(3), 04020004. https://doi.org/10.1061/(asce)cp.1943-5487.0000883

    Article  Google Scholar 

  • Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., & Guo, X. (2022). Real-time vehicle detection based on improved YOLO v5. Sustainability, 14, 12274.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the electronic access to the web-based information service through the library of SVNIT, Surat without the library support, this article would have been much difficult to refer the literature. Moreover, the authors would like to thank the unknown referees for their useful remarks and critical suggestions to improve the quality of the article.

Funding

The authers did not receive support from any organization for submitted work.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Hakim Sabzevari University, Sabzevar, Iran

    Elham Nabizadeh

  2. Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

    Anant Parghi

Authors
  1. Elham Nabizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anant Parghi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

EN: Contributing significantly to the conception of article, study design, execution, acquisition of data, analysis, and interpretation. Writing an original draft, have written and substantially revised the article. AP: Made a significant contribution to conception, investigation, study design, execution, acquisition of data, analysis, and interpretation. Reviewing, and editing an original draft, preparation, supervision, and formal analysis, and finalizing the article.

Corresponding author

Correspondence to Elham Nabizadeh.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nabizadeh, E., Parghi, A. Automated corrosion detection using deep learning and computer vision. Asian J Civ Eng 24, 2911–2923 (2023). https://doi.org/10.1007/s42107-023-00684-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42107-023-00684-4

Keywords

Search

Navigation