Application of Deep Learning in Surface Defect Inspection of Ring Magnets

  • Xu WangEmail author
  • Pan Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11515)


We present a method of inspecting surface defects of ring magnets by using deep learning technology, and the inspection system developed utilizing this method has achieved much better accuracy and speed than human inspectors in actual production environment, while such accuracy and speed are essential for such systems. The proposed method can also be used for the surface defect inspection of many other industrial products and systems.


Machine vision Defect inspection Image processing Deep learning Semantic segmentation Caffe Global convolution network 


  1. 1.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–596 (1996)CrossRefGoogle Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE International conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  3. 3.
    Asha, V., Bhajantri, N.U., Nagabhushan, P.: GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures. IJCVR 2(4), 302–313 (2011)CrossRefGoogle Scholar
  4. 4.
    Asha, V., Bhajantri, N.U., Nagabhushan, P.: Automatic detection of texture defects using texture-periodicity and gabor wavelets. In: Venugopal, K.R., Patnaik, L.M. (eds.) ICIP 2011. CCIS, vol. 157, pp. 548–553. Springer, Heidelberg (2011). Scholar
  5. 5.
    Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Everingham, M., Gool, L.J.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes VOC challenge. IJCV 88(2), 303–38 (2010)Google Scholar
  7. 7.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  8. 8.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv:1511.00561 (2015)
  9. 9.
    Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). Scholar
  10. 10.
    Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large Kernel Matters– Improve Semantic Segmentation by Global Convolutional Network, arXiv:1703.02719
  11. 11.
    Zhang, Z., Zhang, X., Peng, C., Cheng, D., Sun, J.: ExFuse: Enhancing Feature Fusion for Semantic Segmentation, arXiv:1804.03821
  12. 12.
    He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of Tricks for Image Classification with Convolutional Neural Networks, arXiv:1812.01187

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sankyo Precision (Huizhou) Co., LtdHuizhouChina

Personalised recommendations