Surface-mount device light emitting diode (SMD-LED) is characterized by small size, wide viewing angle and light weight. It becomes the main package type of LED gradually. The traditional visual inspection is likely to cause misrecognition due to personal subjectivity and different defect recognition standards. Therefore, this study develops an automatic SMD-LED defect detection system, which is characterized by non-contact inspection, defect recognition standardization and upgrading product quality. It detects the common and important defects in LED package components, including missing component, no chip, wire shift and foreign material. In this study the gray scale characteristic of histogram is used as the rapid sieving analysis indicator of missing component defect, and then the component and solder joint are positioned by using fast normalized cross-correlation, and the maximum correlation coefficient value is used as judgment indicator of no chip defect. In order to overcome the difficult identification as the weld line is subject to light rays, the improved Michelson-like contrast (MLC) enhancement is proposed, and the segmentation threshold is selected by entropy information to segment the weld line successfully. Furthermore, in order to overcome the effect of the tolerance of component size and internal electrode and unfixed weld line position resulted from lead frame process on foreign material detection result, the multiscale adaptive Fourier analysis (MAFA) is proposed in the concept of texture anomaly detection for foreign material defect detection. The result proves that the proposed method can segment the defect effectively and correctly compared with the phase-only transform (PHOT) and multiscale phase-only transform (MPHOT), and it can be used in other fields of texture anomaly detection. The overall recognition rate of this system is 98.25%, contributing to the large market demand and high component quality of LED industry.
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Aiger, D., & Talbot, H. (2010). The phase only transform for unsupervised surface defect detection. IEEE conference on computer vision and pattern recognition, San Francisco, USA (pp. 295–302).
Aldea, E., & Le Hégarat-Mascle, S. (2015). Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework. Journal of Electronic Imaging, 24(6), 061119–061119.
Bai, X., Fang, Y., Lin, W., Wang, L., & Ju, B. F. (2014). Saliency-based defect detection in industrial images by using phase spectrum. IEEE Transactions on Industrial Informatics, 10(4), 2135–2145.
Bürger, F., & Pauli, J. (2013). Unsupervised segmentation of anomalies in sequential data, images and volumetric data using multiscale Fourier phase-only analysis. In Scandinavian conference on image analysis, Espoo, Finland (vol. 7944, pp. 44–53).
Celik, T. (2012). Two-dimensional histogram equalization and contrast enhancement. Pattern Recognition, 45(10), 3810–3824.
Celik, T., & Tjahjadi, T. (2011). Contextual and variational contrast enhancement. IEEE Transactions on Image Processing, 20(12), 3431–3441.
Chang, K. H. (2012). Development of optical inspection system for surface mount device light emitting diodes. Master thesis, National Sun Yat-sen University, Taiwan.
Chen, Z., Zhang, Q., Jiao, F., Chen, R., Wang, K., Chen, M., et al. (2012). Study on the reliability of application-specific LED package by thermal shock testing, failure analysis, and fluid-solid coupling thermo-mechanical simulation. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2(7), 1135–1142.
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2006). Image denoising with block-matching and 3D filtering. Journal of Electronic Imaging, 6064(14), 1–12.
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16(8), 2080–2095.
Hsu, C. C., & Chen, M. S. (2016). Intelligent maintenance prediction system for LED wafer testing machine. Journal of Intelligent Manufacturing, 27(2), 335–342.
Jang, C. Y., Kang, S. J., & Kim, Y. H. (2016). Adaptive contrast enhancement using edge-based lighting condition estimation. Digital Signal Processing, 58, 1–9.
Kim, S. E., Jeon, J. J., & Eom, I. K. (2016). Image contrast enhancement using entropy scaling in wavelet domain. Signal Processing, 127, 1–11.
Kuo, C. F. J., Hsu, C. T. M., Liu, Z. X., & Wu, H. C. (2015). Automatic inspection system of LED chip using two-stages back-propagation neural network. Journal of Intelligent Manufacturing, 25(6), 1235–1243.
Lewis, J. P. (1995). Fast normalized cross correlation. Vision Interface, 10, 120–123.
Li, Q., & Ren, S. (2012). A visual detection system for rail surface defects. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Review, 42(6), 1531–1542.
Lin, H. D., & Chiu, S. W. (2011). Flaw detection of domed surfaces in LED packages by machine vision system. Expert Systems with Applications, 38(12), 15208–15216.
Lin, H. D., & Chiu, Y. S. (2013). An innovative blemish detection system for curved LED lenses. Expert Systems with Applications, 40(2), 471–479.
Perng, D. B., Liu, H. W., & Chang, C. C. (2012). Automated SMD LED inspection using machine vision. International Journal of Advanced Manufacturing Technology, 57(9–12), 1065–1077.
Perng, D. B., Liu, H. W., & Chen, S. H. (2014). A vision-based LED defect auto-recognition system. Nondestructive Testing and Evaluation, 29(4), 315–331.
Photonics Industry and Technology Development Association (PIDA). (2011). Optical communication industry grows nearly 10 % in 2011. PIDA.
Tolba, A. S. (2011). Fast defect detection in homogeneous flat surface products. Expert Systems with Applications, 38, 12339–12347.
Tsai, D. M., & Huang, T. Y. (2003). Automated surface inspection for statistical textures. Image and Vision Computing, 21(4), 307–323.
Tsang, C. S., Ngan, H. Y., & Pang, G. K. (2016). Fabric inspection based on the Elo rating method. Pattern Recognition, 51, 378–394.
The research was supported by the Ministry of Science and Technology of the Republic of China under the Grant No. MOST 104-2221-E-011-156.
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Kuo, C.J., Fang, T., Lee, C. et al. Automated optical inspection system for surface mount device light emitting diodes. J Intell Manuf 30, 641–655 (2019) doi:10.1007/s10845-016-1270-6
- LED package component
- Defect inspection
- Texture inspection
- Adaptive Fourier analysis