Skip to main content
Log in

An Image Mosaic Method for Defect Inspection of Steel Rotary Parts

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

According to the features of the inspection images for the steel rotary parts with defects, a novel image mosaic method, using Scale Invariant Feature Transform (SIFT) feature tracking with purifying feature points based on slope probability measure and RANSAC algorithm, is proposed. First, the method preprocesses the captured sequence images, and then implements projection transformation for these images. Then, the registration parameters for two adjacency images, using the SIFT algorithm and removal algorithm of the pseudo matching feature point pairs based on slope probability measure and RANSAC algorithm, can be solved to mosaic the defect inspection images of the parts with enough characteristic information. On this basis, a hardware-based method is used to perform image stitching of the measured parts. Experimental results show that the method can produce a large number of the correct matching feature point pairs, and can get a seamless, clear surface image of the parts, which will settle the foundation for automatic accurate inspection of the surface defects on metal parts.

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

Access this article

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

Similar content being viewed by others

References

  1. Wang, Y.W.: Key Technology Research and Prototype Development of Steel Ball Surface Defect Detection. Harbin University of Science and Technology, Harbin (2010). (In Chinese)

    Google Scholar 

  2. Geyi, Z., Guohui, W., Jiaying, G., et al.: Study on image mosaic detection algorithm for gun barrel bore image. J. Acad. Armored Force Eng. 21(4), 28–31 (2007). (In Chinese)

    Google Scholar 

  3. Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)

    Article  Google Scholar 

  4. Ou, Y.M., Dung, L.R., Jeng, W.D., et al.: Image stitching and image reconstruction of intestines captured using radial imaging capsule endoscope. Opt. Eng. 51(5), 84–84 (2012)

    Google Scholar 

  5. Rui, L., Juanle, W., Fusheng, G., et al.: Research on automatic registration of remote sensing images using Fourier–Mellin transform. Comput. Eng. Appl 46(16), 178–181 (2010). (In Chinese)

    Google Scholar 

  6. Castro, E.D., Morandi, C.: Registration of translated and rotated image using finite Fourier transforms. IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 700–703 (1987)

    Article  Google Scholar 

  7. Ji, H., Sun, H.H.: SIFT feature matching algorithm with global information. Opt. Precis. Eng. 17(2), 439–444 (2009)

    MathSciNet  Google Scholar 

  8. Venugopala, G., Padmajadevi, G., Tech, M.: Image stitching using speeded up robust features. Int. J. Recent Innov. Trends Comput. Commun. 3(6), 3514–3519 (2015)

    Google Scholar 

  9. Yuning, H., Lin, J., Lin, C.: An improved SIFT feature matching algorithm. In: Proceeding of the IEEE, WCICA, pp. 6109–6113, (2010)

  10. Xiaobing, N., Li, W., Meirong, Z., et al.: Study on the key techniques for the measurement of two-dimensional image mosaic based on feature. Acta Metrol. Sin. 23(2), 147–150 (2002). (In Chinese)

  11. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  12. Dang, J.W., Zong, Y., Wang, Y.P.: Research on image mosaic optimization algorithm based on SIFT feature detection. Appl. Res. Comput. 29(1), 329–332 (2012). (In Chinese)

    Google Scholar 

  13. Psyllos, A.P., Anagnostopoulos, C.N.E., Kayafas, E.: Vehicle logo recognition using a SIFT-based enhanced matching scheme. IEEE Trans. Intell. Transp. Syst. 11(2), 322–328 (2010)

    Article  Google Scholar 

  14. Zhou, A., Guo, J., Shao, W., et al.: A segmental calibration method for a miniature serial-link coordinate measuring machine using a compound calibration artefact. Meas. Sci. Technol. 24(6), 065001 (2013)

    Article  Google Scholar 

  15. Zhou, A., Guo, J., Shao, W., et al.: Multipose measurement of surface defects on rotary metal parts with a combined laser-and-camera sensor. Opt. Eng. 52(10), 190–197 (2013)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Shaanxi Provincial Education Department Foundation under Grant No. 15JK1331, and National Natural Science Foundation of China under Grant No. 51505359.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Awei Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, A., Shao, W. & Guo, J. An Image Mosaic Method for Defect Inspection of Steel Rotary Parts. J Nondestruct Eval 35, 60 (2016). https://doi.org/10.1007/s10921-016-0375-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10921-016-0375-3

Keywords

Navigation