Abstract
The detection of surface defects in industrial production is an important technology for controlling product quality. Many researchers have applied deep learning methods to the field of surface defect detection. However, obtaining defect sample data in industrial production is difficult, and the number of samples available to train detection networks is not sufficient. Based on the you only look once (YOLO) detection system, we propose a lightweight small sample detection network (SSDN) to overcome the problem of fewer samples in surface defect detection. The SSDN is demonstrated to be a suitable network to represent defect image features as it is better at feature extraction and easier to train. We used only 10/type images to train the SSDN model without data enhancement techniques and achieved excellent results (average accuracy 99.72%) on defect detection benchmark data. Experimental results verify the robustness of the model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhou, W., et al.: A sparse representation based fast detection method for surface defect detection of bottle caps. Neurocomputing 123, 406–414 (2014)
Feng, C., Liu, M.Y., Kao, C.C., Lee, T.Y.: Deep active learning for civil infrastructure defect detection and classification. Mitsubishi Electric Research Laboratories (2017). https://www.merl.com/publications/docs/TR2017-034.pdf. Accessed 6 June 2018
Zhang, Y., Li, T., Li, Q.: Defect detection for tire laser stereography image using curvelet transform based edge detector. Opt. Laser Technol. 47(4), 64–71 (2013)
Li, W.C., Tsai, D.M.: Wavelet-based defect detection in solar wafer images with inhomogeneous texture. Pattern Recogn. 45(2), 742–756 (2012)
Mei, S., Yang, H., Yin, Z.: An unsupervised-learning-based approach for automated defect inspection on textured surfaces. IEEE Trans. Instrum. Meas. 67(6), 1266–1277 (2018)
Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18(4), 1064 (2018)
Wang, X., Dong, R., Li, B.: TFT-LCD mura defect detection based on ICA and multi-channels fusion. In: 2016 3rd International Conference on Information Science and Control Engineering (ICISCE), pp. 687–691. IEEE, Beijing (2016)
Chen, J., Liu, Z., Wang, H., Núñez, A., Han, Z.: Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Trans. Instrum. Meas. 67(2), 257–269 (2018)
Lin, H.D.: Tiny surface defect inspection of electronic passive components using discrete cosine transform decomposition and cumulative sum techniques. Image Vis. Comput. 26(5), 603–621 (2008)
Aiger, D., Talbot, H.: The phase only transform for unsupervised surface defect detection. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 295–302. IEEE, San Francisco (2010)
Huang, J.X., Li, D., Ye, F., et al.: Detection of surface defection of solder on flexible printed circuit. Opt. Precis. Eng. 18(11), 2443–2453 (2010)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: International Conference on Computer Vision & Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE, San Diego (2005)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Rätsch, G., Onoda, T., Müller, K.R.: Soft margins for AdaBoost. Mach. Learn. 42(3), 287–320 (2001)
Wang, T., Chen, Y., Qiao, M., Snoussi, H.: A fast and robust convolutional neural network-based defect detection model in product quality control. Int. J. Adv. Manuf. Technol. 94(9–12), 3465–3471 (2018)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE, Columbus (2014)
Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE, Las Vegas (2016)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125. IEEE, Hawaii (2017)
Ren, S., He, K., Girshick, R., Sun, S.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271. IEEE, Hawaii (2017)
Redmon, J., Farhadi, A.: YOLOV3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (ICLR), San Diego (2015)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Pedoeem, J., Huang, R.: YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. arXiv preprint arXiv:1811.05588 (2018)
Weimer, D., Scholz-Reiter, B., Shpitalni, M.: Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann. 65(1), 417–420 (2016)
Siebel, N.T., Sommer, G.: Learning defect classifiers for visual inspection images by neuro-evolution using weakly labelled training data. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3925–3931. IEEE, Hong Kong (2008)
Timm, F., Barth, E.: Non-parametric texture defect detection using Weibull features. In: Image Processing: Machine Vision Applications IV, vol. 7877, p. 78770J. International Society for Optics and Photonics, San Francisco (2011)
Acknowledgments
This work is supported by a grant from the National Natural Sciences Foundation of China (51775214). All the authors are grateful for the funding. In addition, the authors especially thank the contributors to the DAGM2007 surface defect databases.
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
Yu, W., Zhang, Y., Shi, H. (2019). Surface Defect Inspection Under a Small Training Set Condition. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11743. Springer, Cham. https://doi.org/10.1007/978-3-030-27538-9_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-27538-9_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27537-2
Online ISBN: 978-3-030-27538-9
eBook Packages: Computer ScienceComputer Science (R0)