FPGA-based of Thermogram Enhancement Algorithm for Non-destructive Thermal Characterization

Author

Department of Electronic Engineering, Cheongju University, Cheongju, South Korea

Abstract

Thermal imaging technology is used to translate thermal energy or heat into visible light for analyzing the sample images known as a thermogram. It has numerous applications such as for surveillance, medical diagnosis, and other industry which requires a non-contact temperature measurement, etc. The image results of this proposed algorithm show more visible features in terms of the separation between the sampled object and its background. The extraction process used the integrated Otsu method and the high-value thermal algorithm. The color mapping process helps to highlight the necessary characteristics of the sampled thermal images. This work is synthesized using Xilinx Zync 7000 ZED ZC702. The experimental results extracted more significant features and characteristics of the sampled image. In addition, the proposed algorithm shows a faster processing time and minimizes the resource utilization compared with the other methods.

Keywords


1.   Endres, F., Hess J., and Sturm, J. “3-D Mapping with an RGB-D Camera”, IEEE Transactions on Robotics, Vol. 30, No. 1, (2014), 177-187.
2.   Balageas D.L., Deom A.A., Boscher D.M., “Characterization and nondestructive testing of carbon-epoxy composites by a pulsed photothermal method”, Materials Evaluation Vol. 45, (1987), 465–466.
3.   Maldague X., “Theory and Practice of Infrared Technology for Nondestructive Testing”, Wiley-Interscience Publication, John Wiley & Sons Inc (2001).
4.   Lhota J.R., Shepard S.M., Rubadeux B.A., Ahmed T., “Enhanced Spatial and Depth Resolution of Pulsed Thermographic Images”, Review of Progress in Quantitative Nondestructive Evaluation 20A, (2000), 492-498.
5.   Kumar, S. and Mahto D., “Recent Trends in Industrial and Other Engineering Applications of Non-Destructive Testing: A Review”, International Journal of Scientific and Engineering Research, Vol. 4, No. 9, (2013), 183-195.
6.   Dua, G. and Mulaveesala R., “Aperiodic Thermal Wave Imaging Approach for Non-Destructive Testing and Evaluation of Steel Material: A Numerical Study”, Journal of Nanoengineering and Nanomanufacturing, Vol. 6, No. 4, 265-269, 2016.
7.   Mulaveesala R., and Tuli S., “Digitized frequency modulated thermal wave imaging for non-destructive Testing”, Materials Evaluation, Vol. 63, (2005), 1046-50.
8.   Mulaveesala R., and Ghali V.S., “Coded excitation for infrared non-destructive testing of carbon fiber reinforced plastics”, Review of Scientific Instruments Vol.  82, No. 5 (2011): 054902.
9.   Ghali V. S., and Mulaveesala R., “Quadratic frequency modulated thermal wave imaging for non- destructive testing”, Progress In Electromagnetics Research, Vol 26 (2012), 11-22.
10. Zhou, Z., Malone E., Sato dos Santos G., Li N., Xu H., and Holder D., “Comparison of Different Quadratic Regularization for Electrical Impedance Tomography,” Proc. of 6th Conference of the International Federation for Medical and Biological Engineering, (2015), 200-203.
11. Mohd M., Hernan S., and Sharif Z., “Application of K-means clustering in Hot Spot Detection for Thermal Infrared Images”, Proc. of 2017 IEEE Symposium on Computer Applications and Industrial Electronics, (2017), 107-110.
12. Etehadtayakol M., Sadri S., and Ng E.Y., “Application of K and Fuzzy C-means for color segmentation of thermal infrared breast images”, Journal of Medical Systems, Vol 34, No. 1, (2010), 35-42.
13. Zhou H., Soh Y. C., and Wu X., “Integrated analysis of CFD Data with K-means Clustering Algorithm and Extreme Learning Machine for Localized HVAC Control”, Applied Thermal Engineering, Vol. 76, (2015), 98-104.
14. Malay K., Pakhira, A., “Fast K-means algorithm using cluster shifting to produce compact and separate clusters (Research Note)”, International Journal of Engineering, Transactions A: Basic, Vol. 28, No. 1, (2015), 35-43.
15. Shaeiri, Z., and Ghaderi, R., “Modification of the fast global k-means using a fuzzy relation with apllication in microarray data analysis”, International Journal of Engineering, Transactions C: Spects, Vol. 25, No. 4, (2012), 283-292.
16. Mohammadkhanloo M., and Bashiri M., “A clustering based location-allocation problem considering transpotation costs and statistical properties (Research Note)”, International Journal of Engineering, Transactions C: Aspects, Vol. 26, No. 6, (2013), 597-604.
17. Zhou S., Yang P., and Xie W., “Infrared Image Segmentation Based on Otsu and Genetic Algorithm”, Proc. of 2011 International Conference on Multimedia Technology, (2011), 5421-5424.
18. Heriansyah R., and Abu-Bakar S., “Defect Detection in Thermal Image using Thresholding Technique”, Proc. of 6th WSEAS International Conference on Circuits, Systems, Electronics, Control and Signal Processing, (2017), 341-346.
19. Vala H., and Baxi A., “A Review on Otsu Image Segmentation Algorithm”, International Journal of Advanced Research in Computer Engineering and Technology, Vol. 2, No. 2, (2013), 387-389.
20. Lee S. U., Chung S. Y., and Park R. H., “A Comparative Performance Study of Several Global Thresholding Techniques for Segmentation”, Computer Vision, Graphics, and Image Processing, Vol. 52, No. 2, (1990), 171-190.
21. Serfa Juan R. O., and Kim H. S., “Reconfiguration of an FPGA-base Time-Triggered FlexRay Network Controller using EEDC”, Journal of Circuits, Systems, and Computers, Vol. 27, No. 6, (2017), 1-11.
22. Mulaveesala R., “Thermal Non-destructive Testing and Evaluation: Coming of Age”, Journal of Information Technology and Software Engineering, Vol. 3, No. 2, (2013), 1-2.
23. Lhota J. R., Shepard S. M., Rubadeux B.A., and Ahmed T. “Enhanced Spatial and Depth Resolution of Pulsed Thermographic Images”, Review of Progress in Quantitative Nondestructive Evaluation, Vol. 20, (2000), 492-498.
24. Balageas D. L., “Defense and illustration if time-resolved pulsed thermography for NDE”, Journal of Quantitative Infrared Thermography Journal, Vol. 9, No. 1, (2011), 3-32.
25. Aamodt L. C., Maclachlan Spicer J. W., and J. C. Murphy, “Analysis of characteristic thermal transits times for time-resolved infrared radiometry studies of multilayered coatings”, AIP Journal of Applied Physics, Vol. 68, No. 12, (1990), 6087-6098.
26. Maclachlan Spicer J. W., Kerns W. D., Aamodt L.C., and Murphy J. C., “Measurement of coating physical properties and detection of coating disbands by time-resolved infrared radiometry”, Journal of Nondestructive Evaluation, Vol. 8, No. 2, (1989), 107-120.
27. Streza M., Hodisan I., Prejmerean C., Boue C., and Tessier G., “Lock-in thermography, penetrant inspection, and scanning electron microscopy for quantitative evaluation of open micro-cracks at the tooth-restoration interface”, Journal of Physics D; Applied Physics, Vol. 48, No. 10., (2015), pages 1-11.
28. Delanthebettu S., Menaka M., Venkatraman B., and Raj B., “Defect depth quantification using lock-in thermography”, Journal Quantitative Infrared Thermography Journal, Vol. 12, No. 1, (2015), 37-52.
29. Maldague X. P. V., Marinetti, “Pulsed phase thermography”, Journal of Applied Physics, Vol. 79, (1996), 2694-2698.
30. Mohd M., Hernan S., and Sharif Z., “Application of K-means clustering in Hot Spot Detection for Thermal Infrared Images”, Proceeding of 2017 IEEE Symposium on Computer Applications and Industrial Electronics, (2017), 107-110.
31. Etehadtayakol M., Sadri S., and Ng E. Y., “Application of K and Fuzzy C-means for color segmentation of thermal infrared breast images”, Journal of Medical Systems, Vol 34, No. 1, (2010), 35-42.
32. Zhou H, Soh Y. C., and Wu X., “Integrated analysis of CFD Data with K-means Clustering Algorithm and Extreme Learning Machine for Localized HVAC Control”, Applied Thermal Engineering, Vol. 76, (2015), 98-104.
33. Zhou S., Yang P., and Xie W., Infrared Image Segmentation Based on Otsu and Genetic Algorithm, Proceeding of 2011 International Conference on Multimedia Technology, (2011), 5421-5424.
34. Heriansyah R., and Abu-Bakar S., “Defect Detection in Thermal Image using Thresholding Technique”, Proceeding of 6th WSEAS International Conference on Circuits, Systems, Electronics, Control and Signal Processing, (2017), 341-346.
35. Vala H., and Baxi A., “Review on Otsu Image Segmentation Algorithm”, International Journal of Advanced Research in Computer Engineering and Technology, Vol. 2, No. 2, (2013), 387-389.
36. Tzeng C., Yang Z., and Tsai W., “ Data Hiding in Palette Images by Color Ordering and Mapping with Security Protection”, IEEE Transactions on Communications, Vol. 54, No. 5, (2004), 791-800.
37. Faridul H. S., Pouli T., and Chamaret C., “A Survey of Color Mapping and its Applications”, Proceeding of Eurographics 2014, (2014), 1-25.
38. Toet A., and Walraven J., “New false color mapping for image fusion”, Optical Engineering, (1996), 650-658.
39. Hogervirst M., and Toet A., “Improved Color Mapping Methods for Multiband Nighttime Image Fusion”, Journal of Imaging, Vol. 3, No. 3, (2017), 1-25.
40. Han D.S., Serfa Juan R. O., Jung M. W., Cha H.W., and Kim H. S., “Development of a Novel Fast Rotation Angle Detection Algorithm using a Quasi-Rotation Invariant Feature Based on Sobel Edge”, Journal of Telecommunication, Electronic and Computer Engineering, Vol. 9, No. 2-6, (2017), 33-36.
41. Ashour A., Samanta S., Dey N., Kausar N., Abdessalemkaraa W. B, and Hassanien A. E., “Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach”, Journal of Signal and Information Processing, (2015), 244-257.
42. Ghosh A., Sarkar A., Ashour A., Blas-Timar D., Dey N., and Blas V., “Grid Color Moment Features in Glaucoma Classification”, International Journal of Advanced Computer Science and Applications, Vol. 6, No. 9, (2015), 99-107.
43. Ko B. H., and Kim H. S, “Using Enhanced-Color Mapping Algorithm for Object Boundary Segmentation”, International Journal of Applied Engineering Research, Vol 12, No. 15, (2017), 5187-5190.
44. Leedham G., Tan C., Takru K., Tan J. H. N., and Mian L., “Comparison of Some Thresholding Algorithms for Text/Background Segmentation in Difficult Documents Images”, Proceeding of Seventh International Conference on Document Analysis and Recognition,(2003), 1-6.
45. Sezgin M., Sankur B., “Survey Over Image Thresholding Techniques and Quantitative Performance evaluation”, Journal of Electronic Imaging, Vol. 13, No. 1, (2004), 146-164.
46. Cheong Y. K. , Yap V. V. , and Nisar H., “A Novel Face Detection Algorithm using Thermal Imaging”, Proceeding of IEEE 2014 Symposium on Computer Applications and Industrial Electronics, (2014), 208-213.
47. Robinson J., Shearing P., and Brett D., “Thermal Imaging of Electrochemical Power Systems: A Review”, Journal of Imaging, Vol. 2, No. 1, (2016), 1-20.
48. Ring E. F., and Ammer K., “Infrared Thermal Imaging in Medicine”, Physiological Measurement Journal, Vol. 38, (2017), 33-46.
49. Demirel H., and Anbarjafari G., “Image Resolution Enhancement by using Discrete and Stationary Wavelet Decomposition”, IEEE Transactions on Image Processing, Vol. 20, No. 5, (2011), 1458-1460.
50. Goyal, M., “Morphological Image Processing”, International Journal of Computer Science and Technology, Vol. 2, No. 4, (2011), 161-165.
51. Ashourian, M., Deneshmandpour, Sharifi Tehrani, O., and Moallem, P. “Real Time Implementation of a License Plate Location Recognition System Based on Adaptive Morphology”, International Journal of Engineering Transactions B; Applications, Vol. 26, No. 11, (2013), 1347-1356.