Abstract
Defect inspection is a vital part of the production process to control the quality of LED chip. On the one hand, traditional methods are time-consuming, which rely on models badly and require rich operation experience. On the other hand, defect localization cannot be achieved by using traditional networks. To solve these problems, we achieve the application of convolutional neural network (CNN) for LED chip defect inspection. Built in the CNN, a class activation mapping technique is proposed to localize defect regions without using region-level human annotations. Further, LED chip datasets are collected for training the CNN. It is worth to emphasize that the chip defect classification and localization tasks are completed in a single CNN which is very fast and convenient. The proposed CNN based defect inspector named LEDNet achieves impressively high performance on the inspection of LED chip defects (line blemishes and scratch marks) with an inaccuracy of 5.04%, localizing exact defect regions as well.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chang, C. Y., Chang, C. H., Li, C. H., & De Jeng, M. (2007). Learning vector quantization neural networks for LED wafer defect inspection. In Second international conference on innovative computing, information and control, 2007. ICICIC ’07, p. 229.
Choi, K. J., Lee, Y. H., Moon, J. W., Park, C. K., & Harashima, F. (2007). Development of an automatic stencil inspection system using modified Hough transform and fuzzy logic. IEEE Transactions on Industrial Electronics, 54(1), 604–611.
Deng, J., et al. (2009). ImageNet: A large-scale hierarchical image database. In IEEE conference on computer vision and pattern recognition, pp. 248–255.
Everingham, M., Gool, L. V., & Williams, C. K. I. (2010). The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2), 303–338.
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint.
Imamoglu, N., Kimura, M., Miyamoto, H., Fujita, A., & Nakamura, R. (2017). Solar power plant detection on multi-spectral satellite imagery using convolutional neural network with feedback model and m-PCNN fusion. arXiv preprint.
Jia, Y. Q., et al. (2014). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on multimedia, pp. 675–678.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural network. In A. Krizhevsky & I. Sutskever (Eds.), International conference on neural information processing systems, pp. 1097–1105.
Kumar, A. (2008). Computer-vision-based fabric defect detection: A survey. IEEE Transactions on Industrial Electronics, 55(1), 348–363.
Kuo, C. F. J., Hsu, C. T. M., Liu, Z. X., & Wu, H. C. (2014). Automatic inspection system of LED chip using two-stages back-propagation neural network. Journal of Intelligent Manufacturing, 25(6), 1235–1243.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Li, W., Leonardis, A., & Fritz, M. (2016). Visual stability prediction and its application to manipulation. arXiv preprint.
Lin, C. H. (2006). Digital-dimming controller with current spikes elimination technique for LCD backlight electronic ballast. IEEE Transactions on Industrial Electronics, 53(6), 1881–1888.
Lin, H. D. (2009). Automated defect inspection of light-emitting diode chips using neural network and statistical approaches. Expert Systems with Applications, 36(1), 219–226.
Lo, Y. K., Wu, K. H., Pai, K. J., & Chiu, H. J. (2009). Design and implementation of RGB LED drivers for LCD backlight modules. IEEE Transactions on Industrial Electronics, 56(12), 4862–4871.
Otsu, N. (1975). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 11, 23–27.
Ruhmelhart, D. E., Hinton, G. E., & Wiliams, R. J. (1986). Learning representations by back-propagation errors. Nature, 323, 533–536.
Schubert, E. F., Gessmann, T., & Kim, J. K. (2006). Light-emitting diodes. Cambridge: Cambridge Univ. Press.
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M ., Fergus, R., & LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint.
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv preprint.
Szegedy, C., et al. (2015). Going deeper with convolutions. In The IEEE conference on computer vision and pattern recognition, pp. 1–9.
Szeliski, R. (2010). Computer vision: Algorithms and applications. London: Springer Science Business Media.
Tsai, D. M., Chiang, I. Y., & Tsai, Y. H. (2012). A shift-tolerant dissimilarity measure for surface defect detection. IEEE Transactions on Industrial Informatics, 8(1), 128–137.
Wendt, M., & Andriesse, J. W.(2006). LEDs in real lighting applications: From niche markets to general lighting. In Industry Applications Conference, 2006. 41st IAS Annual Meeting. Conference Record of the 2006 IEEE, Vol. 5, pp. 2601–2603.
Wu, X. K., Hu, C., Zhang, J. M., & Zhao, C. (2014). Series-parallel autoregulated charge-balancing rectifier for multioutput light-emitting diode driver. IEEE Transactions on Industrial Electronics, 61(3), 1262–1268.
Wu, C. Y., Wu, T. F., Tsai, J. R., Chen, Y. M., & Chen, C. C. (2008). Multistring LED backlight driving system for LCD panels with color sequential display and area control. IEEE Transactions on Industrial Electronics, 55(10), 3791–3800.
Zhong, F. Q., He, S. P., & Li, B. (2017). Blob analyzation-based template matching algorithm for LED chip localization. The International Journal of Advanced Manufacturing Technology, 93, 55–63.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In The IEEE conference on computer vision and pattern recognition, pp. 2921–2929.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lin, H., Li, B., Wang, X. et al. Automated defect inspection of LED chip using deep convolutional neural network. J Intell Manuf 30, 2525–2534 (2019). https://doi.org/10.1007/s10845-018-1415-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-018-1415-x