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access icon openaccess Improved bare PCB defect detection approach based on deep feature learning

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References

    1. 1)
      • 1. Celik, H.I., Dulger, L.C., Topalbekiroglu, M.: ‘Development of a machine vision system: real-time fabric defect detection and classification with neural networks’, J. Text. Inst., 2014, 105, (6), pp. 575585.
    2. 2)
      • 2. Feng, H., Jiang, Z.G., Xie, F.Y., et al: ‘Automatic fastener classification and defect detection in vision-based railway inspection systems’, IEEE Trans. Instrum. Meas., 2014, 63, (4), pp. 877888.
    3. 3)
      • 3. Koch, C., Georgieva, K., Kasireddy, V., et al: ‘A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure’, Adv. Eng. Inf., 2015, 29, (2), pp. 196210.
    4. 4)
      • 4. Shanmugamani, R., Sadique, M., Ramamoorthy, B.: ‘Detection and classification of surface defects of gun barrels using computer vision and machine learning’, Measurement, 2015, 60, pp. 222230.
    5. 5)
      • 5. Ibrahim, Z., Al-Attas, S.A.R.: ‘Wavelet-based printed circuit board inspection system’, Int. J. Signal Process., 2004, 1, (1), pp. 7379.
    6. 6)
      • 6. Chang, P.C., Chen, L.Y., Fan, C.Y.: ‘A case-based evolutionary model for defect classification of printed circuit board images’, J. Intell. Manuf., 2008, 19, (2), pp. 203214.
    7. 7)
      • 7. Khalid, N.K., Ibrahim, Z., Abidin, M.S.Z., et al: ‘An algorithm to group defects on printed circuit board for automated visual inspection’, Int. J. Simul. Syst. Sci. Technol., 2008, 9, (2), pp. 110.
    8. 8)
      • 8. Ibrahim, I., Ibrahim, Z., Khalil, K., et al: ‘An algorithm for classification of five types of defect on bare printed circuit board’, Int. J. Comput. Sci. Eng. Syst., 2011, 5, (3), pp. 201208.
    9. 9)
      • 9. Putera, S.H.I., Ibrahim, Z.: ‘Printed circuit board defect detection using mathematical morphology and MATLAB image processing tools’. Proc. Int. Conf. on Education Technology and Computer, Shanghai, China, June 2010, pp. V5-359V5-363.
    10. 10)
      • 10. Putera, S.H.I., Dzafaruddin, S.F., Mohamad, M.: ‘Matlab based defect detection and classification of printed circuit board’. Proc. Int. Conf. on Digital Image Computing Techniques and Applications, Bangkok, Thailand, May 2012, pp. 115119.
    11. 11)
      • 11. Ray, S., Mukherjee, J.: ‘A hybrid approach for detection and classification of the defects on printed circuit board’, Int. J. Comput. Appl., 2015, 121, (12), pp. 4248.
    12. 12)
      • 12. Li, Y.F., Li, S.Y.: ‘Defect detection of bare printed circuit boards based on gradient direction information entropy and uniform local binary patterns’, Circuit World, 2017, 43, (4), pp. 145151.
    13. 13)
      • 13. LeCun, Y., Boser, B.E., Denker, J.S., et al: ‘Back propagation applied to handwritten zip code recognition’, Neural Comput., 1989, 1, (4), pp. 541551.
    14. 14)
      • 14. Deng, J., Dong, W., Socher, R., et al: ‘ImageNet: a large-scale hierarchical image database’. Proc. Int. Conf. on Computer Vision and Pattern Recognition, Miami, FL, USA, June 2009, pp. 248255.
    15. 15)
      • 15. Donahue, J., Jia, Y.Q., Vinyals, O., et al: ‘Decaf: a deep convolutional activation feature for generic visual recognition’. Proc. Int. Conf. on Machine Learning, Beijing, China, June 2014, pp. 647655.
    16. 16)
      • 16. Razavian, A.S., Azizpour, H., Sullivan, J., et al: ‘CNN features off-the-shelf: an astounding baseline for recognition’. Proc. Int. Conf. on Computer Vision and Pattern Recognition Workshop, Columbus, OH, USA, June 2014, pp. 512519.
    17. 17)
      • 17. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’, arXiv preprint arXiv:1409.1556, 2014.
    18. 18)
      • 18. Lowe, D.G.: ‘Object recognition from local scale-invariant features’. Proc. Int. Conf. on Computer Vision, Kerkyra, Greece, Sept. 1999, pp. 11501157.
    19. 19)
      • 19. Lowe, D.G.: ‘Distinctive image features from scale-invariant keypoints’, Int. J. Comput. Vis., 2004, 60, (2), pp. 91110.
    20. 20)
      • 20. He, D.C., Wang, L.: ‘Texture unit, texture spectrum, and texture analysis’, IEEE Trans. Geosci. Remote Sens., 1990, 28, (4), pp. 509512.
    21. 21)
      • 21. Wang, L., He, D.C.: ‘Texture classification using texture spectrum’, Pattern Recognit., 1990, 23, (8), pp. 905910.
    22. 22)
      • 22. Csurka, G., Dance, C., Fan, L.X., et al: ‘Visual categorization with bags of keypoints’. Workshop on Statistical Learning in Computer Vision, ECCV, Prague, Czech Republic, May 2004, vol. 1, (1–22), pp. 12.
    23. 23)
      • 23. Sivic, J., Zisserman, A.: ‘Video Google: a text retrieval approach to object matching in videos’. Proc. Int. Conf. on Computer Vision, Nice, France, October 2003, pp. 14701477.
    24. 24)
      • 24. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘ImageNet classification with deep convolutional neural networks’. Proc. Int. Conf. on Neural Information Processing Systems, Lake, Tahoe, USA, December 2012, pp. 10971105.
    25. 25)
      • 25. Russakovsky, O., Deng, J., Su, H., et al: ‘ImageNet large scale visual recognition challenge’, Int. J. Comput. Vis., 2015, 115, (3), pp. 211252.
    26. 26)
      • 26. Szegedy, C., Liu, W., Jia, Y.Q., et al: ‘Going deeper with convolutions’. Proc. Int. Conf. on Computer Vision and Pattern Recognition, Boston, MA, USA, June 2015, pp. 19.
    27. 27)
      • 27. Rowley, H.A., Baluja, S., Kanade, T.: ‘Human face detection in visual scenes’. Proc. Int. Conf. on Neural Information Processing Systems, Denver, USA, December 1996, pp. 875881.
    28. 28)
      • 28. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. Proc. Int. Conf. on Computer Vision and Pattern Recognition, San Diego, CA, USA, June 2005, pp. 886893.
    29. 29)
      • 29. Ferrari, V., Fevrier, L., Jurie, F., et al: ‘Groups of adjacent contour segments for object detection’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (1), pp. 3651.
    30. 30)
      • 30. Ma, J.J.: ‘Defect detection and recognition of bare PCB based on computer vision’. Chinese Control Conf., Dalian, China, July 2017, pp. 1102311028.
    31. 31)
      • 31. Everingham, M., Van Gool, L., Williams, C.K., et al: ‘The Pascal visual object classes (VOC) challenge’, Int. J. Comput. Vis., 2010, 88, (2), pp. 303338.
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