Skip to main content
SpringerLink
Log in

Image-based automatic multiple-damage detection of concrete dams using region-based convolutional neural networks

  • Original Paper
  • Published:
Journal of Civil Structural Health Monitoring Aims and scope Submit manuscript

Abstract

Detecting damage to concrete dams, such as cracks, spalling, and precipitates, using traditional methods is challenging. In this study, we propose an automatic multiple-damage detection method for concrete dams based on faster region-based convolutional neural networks, which is an end-to-end object detection algorithm that can predict object categories and bounds. In this study, Residual Network-101 is fine-tuned and used to replace the original feature extraction network to enhance the feature-extraction capability. To improve the performance of the model for dam-damage detection, scales and aspect ratios are specifically designed based on the characteristics of dam damage. The proposed damage-detection approach is verified using a dataset containing 912 images (900 × 600 pixels) obtained from different concrete dams. The results reveal that the proposed method can efficiently identify and locate all types of damage in the dam images. The average detection accuracy for cracks, spalling, and precipitates is 0.868, 0.948, and 0.847, respectively. This study can provide reliable technical support for the safe operation of concrete dams.

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
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Li MC, Shen Y, Ren QB, Li H (2019) A new distributed time series evolution prediction model for dam deformation based on constituent elements. Adv Eng Inform 39:41–52

    Article  Google Scholar 

  2. Wu ZR, Gu CS (2005) Trouble detection and health diagnosis of large hydraulic concrete structure. Higher Education Press, Beijing (in Chinese)

    Google Scholar 

  3. Su HZ, Li JY, Wen ZP, Zhou FF (2018) A bi-criteria combined evaluation approach for reinforcement effect of gravity dam with cracks. Int J Solids Struct 147:238–253

    Article  Google Scholar 

  4. Leung CKY, Olson N, Wan KT, Meng AD (2005) Theoretical modeling of signal loss versus crack opening for a novel crack sensor. J Eng Mech 131:777–790

    Google Scholar 

  5. Zhao SZ, Kang F, Li JJ, Ma CB (2021) Structural health monitoring and inspection of dams based on UAV photogrammetry with image 3D reconstruction. Automat Constr 130:103832

    Article  Google Scholar 

  6. Bao TF, Wang JL, Yao YA (2010) A fiber optic sensor for detecting and monitoring cracks in concrete structures. Sci China Technol Sc 53:3045–3050

    Article  Google Scholar 

  7. Zhao JL, Bao TF, Amjad U (2015) Optical fiber sensing of small cracks in isotropic homogeneous materials. Sensor Actuat a-Phys 225:133–138

    Article  Google Scholar 

  8. Zhao ZY, Chen B, Wu ZR, Zhang S (2021) Multi-sensing investigation of crack problems for concrete dams based on detection and monitoring data: a case study. Measurement 175:109137

    Article  Google Scholar 

  9. Chevva K, Shirke JM, Ghosh N (2008) Assessment of concrete quality using non-destructive techniques, Ghatghar project, Maharashtra, India. B Eng Geol Environ 67:65–70

    Article  Google Scholar 

  10. Hui L, Haitao M (2011) Application of ground penetrating radar in dam body detection. Procedia Engineering 26:1820–1826

    Article  Google Scholar 

  11. Feng DM, Feng MQ (2018) Computer vision for SHM of civil infrastructure: from dynamic response measurement to damage detection: a review. Eng Struct 156:105–117

    Article  Google Scholar 

  12. Cha YJ, You K, Choi W (2016) Vision-based detection of loosened bolts using the Hough transform and support vector machines. Automat Constr 71:181–188

    Article  Google Scholar 

  13. Sinha SK, Fieguth PW (2006) Automated detection of cracks in buried concrete pipe images. Automat Constr 15:58–72

    Article  Google Scholar 

  14. Abdel-Qader I, Pashaie-Rad S, Abudayyeh O, Yehia S (2006) PCA-Based algorithm for unsupervised bridge crack detection. Adv Eng Softw 37:771–778

    Article  Google Scholar 

  15. Li G, Zhao XX, Du K, Ru F, Zhang YB (2017) Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine. Automat Constr 78:51–61

    Article  Google Scholar 

  16. Fan XN, Wu JJ, Shi PF, Zhang XW, Xie YJ (2018) A novel automatic dam crack detection algorithm based on local-global clustering. Multimed Tools Appl 77:26581–26599

    Article  Google Scholar 

  17. Shi PF, Fan XN, Ni JJ, Wang GR (2016) A detection and classification approach for underwater dam cracks. Struct Health Monit 15:541–554

    Article  Google Scholar 

  18. Ye XW, Jin T, Yun CB (2019) A review on deep learning-based structural health monitoring of civil infrastructures. Smart Struct Syst 24:567–585

    Google Scholar 

  19. Spencer BF, Hoskere V, Narazaki Y (2019) Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering-Prc 5:199–222

    Google Scholar 

  20. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  21. Dorafshan S, Thomas RJ, Maguire M (2018) Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr Build Mater 186:1031–1045

    Article  Google Scholar 

  22. Cha YJ, Choi W, Buyukozturk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput-Aided Civ Inf 32(5):361–378

    Article  Google Scholar 

  23. Xu Y, Bao YQ, Chen JH, Zuo WM, Li H (2019) Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images. Struct Health Monit 18:653–674

    Article  Google Scholar 

  24. Hoang ND, Nguyen QL, Tran VD (2018) Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network. Automat Constr 94:203–213

    Article  Google Scholar 

  25. Li G, Ren XL, Qiao WT, Ma B, Li Y (2020) Automatic bridge crack identification from concrete surface using ResNeXt with postprocessing. Struct Control Hlth 27(11):e2620

    Article  Google Scholar 

  26. Gopalakrishnan K, Khaitan SK, Choudhary A, Agrawal A (2017) Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Constr Build Mater 157:322–330

    Article  Google Scholar 

  27. Atha DJ, Jahanshahi MR (2018) Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Struct Health Monit 17:1110–1128

    Article  Google Scholar 

  28. Wang NN, Zhao XF, Zhao P, Zhang Y, Zou Z, Ou JP (2019) Automatic damage detection of historic masonry buildings based on mobile deep learning. Automat Constr 103:53–66

    Article  Google Scholar 

  29. Xu Y, Wei SY, Bao YQ, Li H (2019) Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network. Struct Control Hlth 26(3):e2313

    Article  Google Scholar 

  30. Cha YJ, Choi W, Suh G, Mahmoudkhani S, Buyukozturk O (2018) Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput-Aided Civ Inf 33(9):731–747

    Article  Google Scholar 

  31. Xue YD, Li YC (2018) A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects. Comput-Aided Civ Inf 33(8):638–654

    Article  Google Scholar 

  32. Hoskere V, Narazaki Y, Hoang TA, Spencer BF (2020) MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure. J Civ Struct Health 10:757–773

    Article  Google Scholar 

  33. Bang S, Park S, Kim H, Kim H (2019) Encoder-decoder network for pixel-level road crack detection in black-box images. Comput-Aided Civ Inf 34(8):713–727

    Article  Google Scholar 

  34. Chow JK, Su Z, Wu J, Tan PS, Mao X, Wang YH (2020) Anomaly detection of defects on concrete structures with the convolutional autoencoder. Adv Eng Inform 45:101105

    Article  Google Scholar 

  35. Li YT, Bao TF, Chen H, Zhang K, Shu XS, Chen ZX et al (2021) A large-scale sensor missing data imputation framework for dams using deep learning and transfer learning strategy. Measurement 178:109377

    Article  Google Scholar 

  36. Wu ZR, Li J, Gu CS, Su HZ (2007) Review on hidden trouble detection and health diagnosis of hydraulic concrete structures. Sci China Ser E 50:34–50

    Article  Google Scholar 

  37. Akbar MA, Qidwai U, Jahanshahi MR (2019) An evaluation of image-based structural health monitoring using integrated unmanned aerial vehicle platform. Struct Control Hlth 26(1):e2276

    Article  Google Scholar 

  38. Feng CC, Zhang H, Wang HR, Wang S, Li YL (2020) Automatic pixel-level crack detection on dam surface using deep convolutional network. Sensors-Basel 20:2069

    Article  Google Scholar 

  39. Li L, Zhang H, Pang J, et al (2019) Dam surface crack detection based on deep learning. In: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence 738–743

  40. Ren SQ, He KM, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE T Pattern Anal 39:1137–1149

    Article  Google Scholar 

  41. He KM, Zhang XY, Ren SQ, Sun J (2016) Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778

  42. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S et al (2015) ImageNet large scale visual recognition challenge. Int J Comput Vision 115:211–252

    Article  MathSciNet  Google Scholar 

  43. Pan SJ, Yang QA (2010) A survey on transfer learning. IEEE T Knowl Data En 22:1345–1359

    Article  Google Scholar 

  44. Zhuang FZ, Qi ZY, Duan KY, Xi DB, Zhu YC, Zhu HS et al (2021) A comprehensive survey on transfer learning. Proceed IEEE 109(1):43–76

    Article  Google Scholar 

  45. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB et al (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? Ieee T Med Imaging 35:1299–1312

    Article  Google Scholar 

  46. Siddiqi R (2019) Effectiveness of transfer learning and fine tuning in automated fruit image classification. In: Proceedings of the 2019 3rd International Conference on Deep Learning Technologies, pp 91–100

  47. Kumar A, Kim J, Lyndon D, Fulham M, Feng DG (2017) An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J Biomed Health 21:31–40

    Article  Google Scholar 

  48. Najibi M, Samangouei P, Chellappa R, Davis LS (2017) SSH: single stage headless face detector. In: Proceedings of the IEEE international conference on computer vision, pp 4885–4894

  49. Song D, Qiao Y, Corbetta A (2017) Depth driven people counting using deep region proposal network. In: 2017 IEEE International Conference on Information and Automation (ICIA), pp 416–421

  50. Zhang L, Lin L, Liang X, He K (2016) Is faster R-CNN doing well for pedestrian detection? European conference on computer vision. Springer, Cham, pp 443–457

    Google Scholar 

  51. Zhang LL, Zhang Y, Zhang Z, Shen J, Wang HB (2019) Real-time water surface object detection based on improved faster R-CNN. Sensors-Basel 19(16):3523

    Article  Google Scholar 

  52. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

  53. Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1440–1448

  54. Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vision 88:303–338

    Article  Google Scholar 

  55. Everingham M, Zisserman A, Williams CKI et al (2008) The 2005 pascal visual object classes challenge. In: Machine Learning Challenges Workshop. Springer, Heidelberg, pp 117–176

  56. Simonyan. K, Zisserman. A (2015) Very deep convolutional networks for large-scale image recognition. In: Proc. Int. Conf. Learning Represen. arXiv preprint. arXiv:1409.1556

  57. Howard AG, Zhu ML, Chen B, Kalenichenko D, Wang WJ, Weyand T et al (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint. arXiv:1704.04861

  58. Gao SH, Cheng MM, Zhao K, Zhang XY, Yang MH, Torr P (2021) Res2Net: a new multi-scale backbone architecture. IEEE T Pattern Anal 43:652–662

    Article  Google Scholar 

  59. Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. IEEE conference on Computer Vision and Pattern Recognition (Cvpr). arXiv preprint. arXiv:1804.02767

  60. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY et al (2016) SSD: single shot MultiBox detector. Lect Notes Comput Sc 9905:21–37

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key R & D Program of China (2016YFC0401600), the National Natural Science Foundation of China (51779035, 52079022, 51769033 and 51979027), the Fundamental Research Funds for the Central Universities (DUT21TD106).

Author information

Authors and Affiliations

  1. School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, 116024, Dalian, People’s Republic of China

    Ben Huang, Sizeng Zhao & Fei Kang

Authors
  1. Ben Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sizeng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fei Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sizeng Zhao.

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

Huang, B., Zhao, S. & Kang, F. Image-based automatic multiple-damage detection of concrete dams using region-based convolutional neural networks. J Civil Struct Health Monit 13, 413–429 (2023). https://doi.org/10.1007/s13349-022-00650-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13349-022-00650-9

Keywords

Search

Navigation