Deep-Learning-Based Computer Vision System for Surface-Defect Detection
Abstract
Automating optical-inspection systems using machine learning has become an interesting and promising area of research. In particular, the deep-learning approaches have shown a very high and direct impact on the application domain of visual inspection. This paper presents a complete inspection system for automated quality control of a specific industrial product. Both hardware and software part of the system are described, with machine vision used for image acquisition and pre-processing followed by a segmentation-based deep-learning model used for surface-defect detection. The deep-learning model is compared with the state-of-the-art commercial software, showing that the proposed approach outperforms the related method on the specific domain of surface-crack detection. Experiments are performed on a real-world quality-control case and demonstrate that the deep-learning model can be successfully used even when only 33 defective training samples are available. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited.
Notes
Acknowledgements
This work was supported in part by the following research programs: GOSTOP program C3330-16-529000 co-financed by the Republic of Slovenia and the ERDF, ARRS research project J2-9433 (DIVID), and ARRS research programme P2-0214.
References
- 1.Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
- 2.Chen, P.H., Ho, S.S.: Is overfeat useful for image-based surface defect classification tasks? In: IEEE International Conference on Image Processing, pp. 749–753 (2016)Google Scholar
- 3.Cognex: VISIONPRO VIDI: deep learning-based software for industrial image analysis (2018). https://www.cognex.com/products/machine-vision/vision-software/visionpro-vidi
- 4.Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., Schutter, B.D.: Deep convolutional neural networks for detection of rail surface defects deep convolutional neural networks for detection of rail surface defects. In: International Joint Conference on Neural Networks, pp. 2584–2589, October 2016Google Scholar
- 5.Ghazvini, M., Monadjemi, S.A., Movahhedinia, N., Jamshidi, K.: Defect detection of tiles using 2D-wavelet transform and statistical features. Int. Schol. Sci. Res. Innov. 3(1), 773–776 (2009)Google Scholar
- 6.Mak, K.L., Peng, P., Yiu, K.F.: Fabric defect detection using morphological filters. Image Vis. Comput. 27(10), 1585–1592 (2009). https://doi.org/10.1016/j.imavis.2009.03.007CrossRefGoogle Scholar
- 7.Masci, J., Meier, U., Ciresan, D., Schmidhuber, J., Fricout, G.: Steel defect classification with max-pooling convolutional neural networks. In: Proceedings of the International Joint Conference on Neural Networks (2012). https://doi.org/10.1109/IJCNN.2012.6252468
- 8.Rački, D., Tomaževič, D., Skočaj, D.: A compact convolutional neural network for textured surface anomaly detection. In: IEEE Winter Conference on Applications of Computer Vision, pp. 1331–1339 (2018). https://doi.org/10.1109/WACV.2018.00150
- 9.Sermanet, P., Eigen, D.: OverFea: integrated recognition, localization and detection using convolutional networks. In: International Conference on Learning Representations (2014)Google Scholar
- 10.Tabernik, D., Šela, S., Skvarč, J., Skočaj, D.: Segmentation-based deep-learning approach for surface-defect detection. J. Intell. Manuf. 1–18 (2019). https://doi.org/10.1007/s10845-019-01476-x
- 11.Weimer, D., Scholz-Reiter, B., Shpitalni, M.: Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann. - Manuf. Technol. 65(1), 417–420 (2016). https://doi.org/10.1016/j.cirp.2016.04.072CrossRefGoogle Scholar
- 12.Zheng, H., Kong, L.X., Nahavandi, S.: Automatic inspection of metallic surface defects using genetic algorithms. J. Mater. Process. Technol. 125–126, 427–433 (2002). https://doi.org/10.1016/S0924-0136(02)00294-7CrossRefGoogle Scholar