Skip to main content
SpringerLink
Log in

Corrosion inspection and evaluation of crane metal structure based on UAV vision

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Based on the image acquisition of complex steel structures in high altitude by unmanned aerial vehicle, the remote detection and quantitative evaluation method for the surface corrosion defects of crane structures is proposed. Aiming at the corrosion characteristics such as the complex and variable corrosion morphology, the irregular shape, the unbalanced size and complex background, the improved YOLOV3 deep neural network classification model is proposed. The K-means clustering method is used to automatically generate the size of anchor frame to improve the ability of the model to detect the corrosion defects of different sizes and shapes. The model compression and optimization is carried out on the improved YOLOV3 network, and the detection precision reaches 94.31%, and the speed reaches 17 frames per second. Then, the Otsu threshold segmentation and mathematical morphology methods are used to segment the detected corrosion target box area, and the corrosion area is statistically obtained by using the per-pixel method. Considering the weight factor set according to the color depth of the corroded part, the ratio of the corrosion area to the target box area is calculated as the weighted corrosion percentage, and the corrosion grading is evaluated according to the ASTM standard. Qualitative and quantitative tests of the proposed methods are performed, showing that the corrosion detection and evaluation method proposed in this paper can obtain better results than certain existing methods for the corrosion inspection.

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

Similar content being viewed by others

References

  1. L. Ye,W. Li and C. He,et al.Research on Detection Method of Tower Corrosion Based on Hough Transform[C]. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, 2020, pp. 433–436.

  2. S. A. Idris and F. A. Jafar.Image Enhancement Based on Software Filter Optimization for Corrosion Inspection[C]. 2014 5th International Conference on Intelligent Systems, Modelling and Simulation, Langkawi, 2014, pp. 345–350.

  3. Medeiros, N.S., Ramalho, L.B., Bento, P., et al.: On the evaluation of texture and color features for nondestructive corrosion detection[J]. EURASIP J. Adv. Signal Process. 2010, 817473 (2010)

    Article  Google Scholar 

  4. Ji, Y., Wang, S., Lu, Y., Wei, J., Zhao, Y.: Eye and mouth state detection algorithm based on contour feature extraction. J. Electron. Imag. 27(5), 051205 (2018)

    Article  Google Scholar 

  5. Vachtsevanos, G.: Corrosion diagnostic and prognostic technologies. In: Vachtsevanos, G., Natarajan, K., Rajamani, R., Sandborn, P. (eds.) Corrosion processes structural integrity. Springer (2020)

    Chapter  Google Scholar 

  6. L. Ye, W. Li and C. He, "Research on Detection Method of Tower Corrosion Based on Hough Transform," 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, 2020, pp. 433–436.

  7. H. Ling, L. Yong-an and L. Wen-Guang, "Research on Segmentation Features in Visual Detection of Rust on Bridge Pier," 2020 Chinese Control And Decision Conference (CCDC), Hefei, China, 2020, pp. 2084-2088

  8. S. K. Fondevik, A. Stahl, A. A. Transeth and O. Ø. Knudsen, "Image Segmentation of Corrosion Damages in Industrial Inspections," 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), Baltimore, MD, USA, 2020, pp. 787–792.

  9. Ahuja, S.K., Shukla, M.K.: A survey of computer vision based corrosion detection approaches. In: Satapathy, S., Joshi, A. (eds.) Information and communication technology for intelligent systems (ICTIS 2017)–Volume 2. ICTIS 2017. smart innovation, systems and technologies. Springer, Cham (2018)

    Google Scholar 

  10. Liao, K.W., Lee, Y.T.: Detection of rust defects on steel bridge coatings via digital image recognition[J]. Autom. Constr. 71, 294–306 (2016)

    Article  Google Scholar 

  11. Gamarra Acosta, M.R., Vélez Díaz, J.C., Castro, N.S.: An innovative image-processing model for rust detection using Perlin Noise to simulate oxide textures[J]. Corr. Sci. 88, 141–151 (2014)

    Article  Google Scholar 

  12. Y. Zhang, H. Shi, X. Zhou and Z. Zheng, "Vibration analysis approach for corrosion pitting detection based on SVDD and PCA," 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, 2015, pp. 1534–1538.

  13. Jiménez-Come M.J. et al. (2011) Austenitic Stainless Steel EN 1.4404 Corrosion Detection Using Classification Techniques. In: Corchado E., Snášel V., Sedano J., Hassanien A.E., Calvo J.L., Ślȩzak D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg.

  14. Zhang, X., Saniie, J., Heifetz, A.: Detection of defects in additively manufactured stainless steel 316L with compact infrared camera and machine learning algorithms. JOM 72, 4244–4253 (2020)

    Article  Google Scholar 

  15. Alves, L.M., Cotta, R.A., Ciarelli, P.M., et al.: Identification of corrosive substances and types of corrosion through electrochemical noise using signal processing and machine learning. J. Control Autom. Electr. Syst. 30, 16–26 (2019)

    Article  Google Scholar 

  16. Wang, S., Wu, X., Zhang, Y., et al.: A neural network ensemble method for effective crack segmentation using fully convolutional networks and multi-scale structured forests. Mach. Vis. Appl. 31, 60 (2020)

    Article  Google Scholar 

  17. Miao, R., Jiang, Z., Zhou, Q., et al.: Online inspection of narrow overlap weld quality using two-stage convolution neural network image recognition. Mach. Vis. Appl. 32, 27 (2021)

    Article  Google Scholar 

  18. Yang, J., Li, S., Wang, Z., Yang, G.: Real-time tiny part defect detection system in manufacturing using deep learning. IEEE Access 7, 89278–89291 (2019)

    Article  Google Scholar 

  19. X. Xu, Y. Wang, J. Wu and Y. Wang.Intelligent corrosion detection and rating based on faster region-based convolutional neural network[C].2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai), Shanghai, 2020, pp. 1–5.

  20. Z. Bahrami, R. Zhang, R. Rayhana,et al.Optimized Deep Neural Network Architectures with Anchor Box optimization for Shipping Container Corrosion Inspection[C].2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020, pp. 1328–1333.

  21. Yao, Y., Yang, Y., Wang, Y.P., et al.: Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network[J]. Appl. Ocean Res. 90, 101823 (2019)

    Article  Google Scholar 

  22. X. Chen and H. Zhang, "Rail Surface Defects Detection Based on Faster R-CNN," 2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), Tianjin, China, 2020, pp. 819–822.

  23. Y. Wang, Z. Li, X. Yang, N. Luo, Y. Zhao and G. Zhou, "Insulator Defect Recognition Based on Faster R-CNN," 2020 International Conference on Computer, Information and Telecommunication Systems (CITS), Hangzhou, China, 2020, pp. 1–4.

  24. T. Shi, M. Liu, Y. Yang,et al.Fast Classification and Detection of Marine Targets in Complex Scenes with YOLOv3[C].OCEANS 2019 - Marseille, Marseille, France, 2019, pp. 1-5

  25. B. Kilic, E. Baykal, M. Ekinci ,et al.Automated Nuclei Detection on Pleural Effusion Cytopathology Images using YOLOv3[C].2019 4th International Conference on Computer Science and Engineering (UBMK), Samsun, Turkey, 2019, pp. 1–5.

  26. T. ZHANG, X. ZHANG, J. SHI ,et al.High-Speed Ship Detection in SAR Images by Improved Yolov3[C]. 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing, Chengdu, China, 2019, pp. 149–152.

  27. CH. Y. Shih, S. L. Hung, J. H. Garrett Jr. , et al. Steel bridge corrosion detection by wavelet transform theory[C]. Joint International Conference on Computing and Decision Making in Civil and Building Engineering, Montréal, Canada, 2006:2085–2094.

  28. Chen, P.H., Shen, H.K., Lei, C.Y., et al.: Support-vector-machine-based method for automated steel bridge rust assessment[J]. Autom. Constr. 23(May), 9–19 (2012)

    Article  Google Scholar 

Download references

Acknowledgments

We acknowledge the financial support that is given under the Project supported by National Key Research and Development Program of China (2018YFC0809005).

Author information

Authors and Affiliations

  1. Nanjing Special Equipment Safety Supervision Inspection and Research Institute, Nanjing, 210000, China

    Qianfei Zhou, Shuqing Ding, Yuegui Feng, Guangwei Qing & Jingbo Hu

Authors
  1. Qianfei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuqing Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuegui Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guangwei Qing
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jingbo Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qianfei Zhou.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Q., Ding, S., Feng, Y. et al. Corrosion inspection and evaluation of crane metal structure based on UAV vision. SIViP 16, 1701–1709 (2022). https://doi.org/10.1007/s11760-021-02126-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-02126-7

Keywords

Search

Navigation