Advertisement

ScratchNet: Detecting the Scratches on Cellphone Screen

  • Zhao Luo
  • Xiaobing Xiao
  • Shiming GeEmail author
  • Qiting Ye
  • Shengwei Zhao
  • Xin Jin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 773)

Abstract

In the process of cellphone screen manufacture, equipment failures and human errors may lead to screen scratches. Traditional manual ways check scratches by human eyes, which often costs large manpower and time, but with poor effectiveness. To address this issue, we proposes an automated scratches detection method by cascading two main modules. First, the scratch filtering module detects big scratches and localizes small scratches candidates with a serial of low-level stages. Then, the scratches classification module applies a lightweight CNN model, ScratchNet, to identify each small scratch candidate whether it is real scratch or not. To train the ScratchNet, we build a Scratches on Cellphone Screen (SCS) dataset with 50K samples in 5 categories. The experimental results on SCS testing set show that the proposed method achieves an accuracy of \(96.35\%\) on classifying small scratches, which outperforms the LeNet model and the other classifiers.

Keywords

Scratch detection Product quality inspection Cellphone screen Convolutional neural networks 

Notes

Acknowledgments

This work is supported in part by the National Key Research and Development Plan (Grant No. 2016YFC0801005), the National Natural Science Foundation of China (Grant No. 61402463) and Open Foundation Project of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province in China (Grant No. 16kftk01).

References

  1. 1.
    Chiou, Y.C., Li, W.C.: Flaw detection of cylindrical surfaces in PU-packing by using machine vision technique. Measurement 42(7), 989–1000 (2009)CrossRefGoogle Scholar
  2. 2.
    Choi, J., Kim, C.: Unsupervised detection of surface defects: a two-step approach. In: IEEE International Conference on Image Processing, pp. 1037–1040 (2012)Google Scholar
  3. 3.
    Clark, R.: Rail flaw detection: overview and needs for future developments. NDT E Int. 37(2), 111–118 (2004)CrossRefGoogle Scholar
  4. 4.
    Coulombe, A., Cantin, M., Bérard, L., Gauthier, J.: Method and system for detecting defects on a printed circuit board (2004)Google Scholar
  5. 5.
    Dan, C., Meier, U., Masci, J., Schmidhuber, J.: A committee of neural networks for traffic sign classification. 42(4), 1918–1921 (1921)Google Scholar
  6. 6.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation, pp. 580–587 (2014)Google Scholar
  7. 7.
    Goodfellow, I.J., Bulatov, Y., Ibarz, J., Arnoud, S., Shet, V.: Multi-digit number recognition from street view imagery using deep convolutional neural networks. Comput. Sci (2013)Google Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  10. 10.
    LeCan, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  11. 11.
    Liang, L.Q., Li, D., Fu, X., Zhang, W.J.: Touch screen defect inspection based on sparse representation in low resolution images. Multimedia Tools Appl. 75(5), 2655–2666 (2016)CrossRefGoogle Scholar
  12. 12.
    Loh, H.H., Lu, M.S.: Printed circuit board inspection using image analysis. IEEE Trans. Ind. Appl. 35(2), 426–432 (1999)CrossRefGoogle Scholar
  13. 13.
    Mcgrail, A., Risino, A., Auckland, D.W., Varlow, B.R.: Use of a medical ultrasonic scanner for the inspection of high voltage insulation. IEEE Electr. Insul. Mag. 9(6), 5–10 (2002)CrossRefGoogle Scholar
  14. 14.
    Pei, K.: Study of on-line glass defect inspection system based on embedded image processing. Electron. Meas. Technol (2009)Google Scholar
  15. 15.
    Peng, X., Chen, Y., Yu, W., Zhou, Z., Sun, G.: An online defects inspection method for float glass fabrication based on machine vision. Int. J. Adv. Manuf. Technol. 39(11–12), 1180–1189 (2008)CrossRefGoogle Scholar
  16. 16.
    Perng, D.B., Liu, H.W., Chang, C.C.: Automated SMD LED inspection using machine vision. Int. J. Adv. Manuf. Technol. 57(9–12), 1065–1077 (2011)CrossRefGoogle Scholar
  17. 17.
    Shang, H.C., Chen, Y.P., Yu, W.Y., Zhou, Z.D.: Online auto-detection method and system of presswork quality. Int. J. Adv. Manuf. Technol. 33(7–8), 756–765 (2006)Google Scholar
  18. 18.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci (2014)Google Scholar
  19. 19.
    Soukup, D., Huber-Mörk, R.: Convolutional neural networks for steel surface defect detection from photometric stereo images. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., McMahan, R., Jerald, J., Zhang, H., Drucker, S.M., Kambhamettu, C., El Choubassi, M., Deng, Z., Carlson, M. (eds.) ISVC 2014. LNCS, vol. 8887, pp. 668–677. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-14249-4_64 Google Scholar
  20. 20.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  21. 21.
    Verikas, A., Lundström, J., Bacauskiene, M., Gelzinis, A.: Advances in computational intelligence-based print quality assessment and control in offset colour printing. Expert Sys. Appl. 38(10), 13441–13447 (2011)CrossRefGoogle Scholar
  22. 22.
    Zhao, J., Kong, Q.J., Zhao, X., Liu, J., Liu, Y.: A method for detection and classification of glass defects in low resolution images. In: International Conference on Image and Graphics, pp. 642–647 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Zhao Luo
    • 1
    • 2
  • Xiaobing Xiao
    • 3
  • Shiming Ge
    • 1
    • 2
    Email author
  • Qiting Ye
    • 1
    • 2
  • Shengwei Zhao
    • 1
    • 2
  • Xin Jin
    • 4
  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.School of Software and MicroelectronicsPeking UniversityBeijingChina
  4. 4.Department of Computer Science and TechnologyBeijing Electronic Science and Technology InstituteBeijingChina

Personalised recommendations