Abstract
In recent years, convolutional neural network has become a solution to many image processing problems due to high performance. It is particularly useful for applications in automated optical inspection systems related to industrial applications. This paper proposes a system that combines the defect information, which is meta data, with the defect image by modeling. Our model for classification consists of a separate model for embedding location information in order to utilize the defective locations classified as defective candidates and ensemble with the model for classification to enhance the overall system performance. The proposed system incorporates class activation map for preprocessing and augmentation for image acquisition and classification through optical system, and feedback of classification performance by constructing a system for defect detection. Experiment with real-world dataset shows that the proposed system achieved 97.4% accuracy and through various other experiments, we verified that our system is applicable.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Sun, J., Wang, P., Luo, Y.K., Li, W.: Surface defects detection based on adaptive multiscale image collection and convolutional neural networks. IEEE Trans. Instrum. Measur. 1–11 (2019)
Natarajan, V., Hung, T.Y., Vaikundam, S., Chia, L.T.: Convolutional networks for voting-based anomaly classification in metal surface inspection. In: IEEE International Conference on Industrial Technology, pp. 986–991 (2017)
Chen, T., Wang, Y., Xiao, C., Wu, Q.J.: A machine vision apparatus and method for can-end inspection. IEEE Trans. Instrum. Measur. 65, 2055–2066 (2016)
Cao, G., Ruan, S., Peng, Y., Huang, S., Kwok, N.: Large-complex-surface defect detection by hybrid gradient threshold segmentation and image registration. IEEE Access 6, 36235–36246 (2018)
Jian, C., Gao, J., Ao, Y.: Automatic surface defect detection for mobile phone screen glass based on machine vision. Appl. Soft Comput. 52, 348–358 (2017)
Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9, 889–896 (2000)
Gupta, E., Kushwah, R.S.: Combination of global and local features using DWT with SVM for CBIR. In: International Conference on Reliability, Infocom Technologies and Optimization, pp. 1–6 (2015)
Zhou, B., Khosla, A., Lapedriza, A., Olivia, A., Torralba, A.: Learning deep features for discriminative localization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
Borwankar, R., Ludwig, R.: An optical surface inspection and automatic classification technique using the rotated wavelet transform. IEEE Trans. Instrum. Measur. 67, 690–697 (2018)
Dong, X., Taylor, C.J., Cootes, T.F.: Small defect detection using convolutional neural network features and random forests. In: European Conference on Computer Vision, pp. 1–15 (2018)
Fu, G., et al.: A deep-learning-based approach for fast and robust steel surface defects classification. Opt. Lasers Eng. 121, 397–405 (2019)
Yang, H., Chen, Y., Song, K., Yin, Z.: Multiscale feature-clustering-based fully convolutional autoencoder for fast accurate visual inspection of texture surface defects. IEEE Trans. Autom. Sci. Eng. 99, 1–18 (2019)
Staar, B., Lutjen, M., Freitag, M.: Anomaly detection with convolutional neural networks for industrial surface inspection. Procedia CIRP. 79, 484–489 (2019)
Sainath, T., Parada, C.: Convolutional neural networks for small-footprint keyword spotting. In: InterSpeech, pp. 1478–1482 (2015)
Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625–2634 (2015)
Rippel, O., Snoek, J., Adams, R.P.: Spectral representations for convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 2449–2457 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Acknowledgements
This research was supported by Samsung Electronics Co., Ltd.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Go, GM., Bu, SJ., Cho, SB. (2019). A Deep Learning-Based Surface Defect Inspection System for Smartphone Glass. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-33607-3_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33606-6
Online ISBN: 978-3-030-33607-3
eBook Packages: Computer ScienceComputer Science (R0)