Article Article
Two-Stage Automated Defect Recognition Algorithm for the Analysis of Infrared Images

In this article an algorithm for the analysis of raw thermal infrared images is proposed. The images are obtained by using the nondestructive evaluation method of the laser-spot thermography and aim at detecting the presence of surface defects. A laser is used to scan a test specimen through the generation of single pulses. The temperature distribution produced by this thermoelastic source is measured by an infrared camera and processed with a two-stage algorithm. In the first stage, simple mathematical and statistical parameters are used to flag the presence of damage. Then, once damage is detected, the thermal image’s first and second spatial derivative and two spatial filters are computed to enhance contrast, and to locate and size the defect. Some of the advantages of the proposed method with respect to existing approaches include automation in the defect detection process and better defective area isolation through increased contrast. The algorithm is first proven by analyzing simulated thermal images, and then it is experimentally validated by scanning the surface of a CFRP composite plate with induced defects.


1. P. J. Shull. Nondestructive Evaluation: Theory, Techniques, and Applications. Marcel Dekker, New York (2002).

2. C. I. Castanedo, J. M. Piau, S. Guilbert, N. P. Avdelidis, M. Genest, A. Bendada, and X. P. V. Maldague. Research in Nondestructive Evaluation 20(1):1 (2009).

3. X. P. V. Maldague. Nondestructive Testing Handbook: Infrared and Thermal Testing. 3rd ed. American Society for Nondestructive Testing, Columbus, OH (2001).

4. U. Polimeno, M. Meo, D. P. Almond, and S. L. Angiolini. J. Appl. Compos. Master. 17:481–488 (2010).

5. V. P. Vavilov and D. A. Nesteruk. Russ. J. Nondestr. Test 46(2):147–150 (2010).

6. A. Rashed, D. P. Almond, D. A. Rees, S. Burrows, and S. Dixon. Rev. Quant. Nondestr. Eval. 26:500–506 (2007).

7. M. Morbidini, B. Kang, and P. Cawley. J. Materials Evaluation 1193–1202 (2009).

8. M. Morbidini and P. Cawley. Rev. Quant. Nondestr. Eval. 27:536–543 (2008).

9. G. Bolu, A. Gachagan, G. Pierce, and G. Harvey. Rev. Prog. Quant. Nondestr. Eval. 29:474–481 (2010).

10. G. Bolu, A. Gachagan, G. Pierce, G. Harvey, and L. Choong. Rev. Quant. Nondestr. Eval 29:1654–1661 (2010).

11. B. Weekes, P. Cawley, D. P. Almond, and T. Li. Rev. Quant. Nondestr. Eval. 29:490–497 (2010).

12. J. M. Piau, A. H. Bendada, and X. Maldague. J. Mater. Eval. 66(10):1047–1052 (2008).

13. K. A. Tsoi and N. Rajic. 5th Australasian Congress on Applied Mechanics (ACAM). 10–12 December, 2007, Brisbane. Commonwealth of Australia (2007).

14. B. Kang and P. Cawley. Rev. Quant. Nondestr. Eval. 26:484–491 (2007).

15. L. J. Pieczonca, W. J. Staszewwski, F. Aymerich, T. Uhl, and M. Szwedo. IOP Conf. Series: Mater. Sci. Eng. 10:1–8 (2010).

16. S. E. Burrows, A. Rashed, D. P. Almond, and S. Dixon. Rev. Quant. Nondestr. Eval. 29:510–516 (2007).

17. T. Li, D. P. Almond, D. A. S. Rees, B. Weekes, and S. G. Pickering. Rev. Quant. Nondestr. Eval. 29:435–442 (2010).

18. T. Li, D. P. Almond, D. A. S. Rees, and B. Weekes. J. Phys.: Conf. Ser. 214:1–4 (2010).

19. M. Li, S. D. Holland, and W. Q. Meeker. Rev. Quant. Nondestr. Eval. 29:1919–1926 (2010).

20. C. Gao, and W. Q. Meeker. A Statistical Method for Crack Detection from Vibrothermography Inspection Data 1–33 (2010). Available at: (last accessed February 1, 2011).

21. R. C. Gonzales, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB. New York, USA: Prentice-Hall, Inc. Upper Saddle River (2003).

22. R. C. Gonzalez and R. E. Woods. Digital Image Processing. Addison-Wesley Publishing Company, New York (1992).

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