Skip to main content
Log in


Modern inspection systems based on smart sensor technology like image processing and machine vision have been widely spread into several fields of industry such as process control, manufacturing, and robotics applications in factories. Machine learning for smart sensors is a key element for the visual inspection of parts on a product line that has been manually inspected by people. This paper proposes a method for automatic visual inspection of dirties, scratches, burrs, and wears on surface parts. Imaging analysis with CNN (Convolution Neural Network) of training samples is applied to confirm the defect’s existence in the target region of an image. In this paper, we have built and tested several types of deep networks of different depths and layer nodes to select adequate structure for surface defect inspection. A single CNN based network is enough to test several types of defects on textured and non-textured surfaces while conventional machine learning methods are separately applied according to type of each surface. Experiments for surface defects in real images prove the possibility for use of imaging sensors for detection of different types of defects. In terms of energy saving, the experiment result shows that proposed method has several advantages in time and cost saving and shows higher performance than traditional manpower inspection system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others


  1. Aleixos, N., Blasco, J., Navarron, F., and Moltó, E., “Multispectral Inspection of Citrus in Real-Time Using Machine Vision and Digital Signal Processors,” Computers and Electronics in Agriculture, Vol. 33, No. 2, pp. 121–137, 2002.

    Article  Google Scholar 

  2. Espiau, B., Chaumette, F., and Rives, P., “A New Approach to Visual Servoing in Robotics,” IEEE Transactions on Robotics and Automation, Vol. 8, No. 3, pp. 313–326, 1992.

    Article  Google Scholar 

  3. Cheng, Y. and Jafari, M. A., “Vision-Based Online Process Control in Manufacturing Applications,” IEEE Transactions on Automation Science and Engineering, Vol. 5, No. 1, pp. 140–153, 2008.

    Article  Google Scholar 

  4. Funck, J., Zhong, Y., Butler, D., Brunner, C., and Forrer, J., “Image Segmentation Algorithms Applied to Wood Defect Detection,” Computers and Electronics in Agriculture, Vol. 41, No. 1, pp. 157–179, 2003.

    Article  Google Scholar 

  5. Yang, W., Li, D., Zhu, L., Kang, Y., and Li, F., “A New Approach for Image Processing in Foreign Fiber Detection,” Computers and Electronics in Agriculture, Vol. 68, No. 1, pp. 68–77, 2009.

    Article  Google Scholar 

  6. Kumar, A. and Pang, G. K., “Defect Detection in Textured Materials Using Gabor Filters,” IEEE Transactions on Industry Applications, Vol. 38, No. 2, pp. 425–440, 2002.

    Article  Google Scholar 

  7. Tsai, D.-M. and Lai, S.-C., “Defect Detection in Periodically Patterned Surfaces Using Independent Component Analysis,” Pattern Recognition, Vol. 41, No. 9, pp. 2813–2832, 2008.

    Article  MATH  Google Scholar 

  8. Latif-Amet, A., Ertüzün, A., and Erçil, A., “An Efficient Method for Texture Defect Detection: Sub-Band Domain Co-Occurrence Matrices,” Image and Vision Computing, Vol. 18, No. 6, pp. 543–553, 2000.

    Article  Google Scholar 

  9. Cord, A. and Chambon, S., “Automatic Road Defect Detection by Textural Pattern Recognition Based on AdaBoost,” Computer-Aided Civil and Infrastructure Engineering, Vol. 27, No. 4, pp. 244–259, 2012.

    Article  Google Scholar 

  10. Shumin, D., Zhoufeng, L., and Chunlei, L., “AdaBoost Learning for Fabric Defect Detection Based on HOG and SVM,” Proc. of ICMT on Multimedia Technology, pp. 2903–2906, 2011.

    Google Scholar 

  11. Fukushima, K., “Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position,” Biological Cybernetics, Vol. 36, No. 4, pp. 193–202, 1980.

    Article  MATH  Google Scholar 

  12. Le Cun, Y., Bottou, L., Bengio, Y., and Haffner, P., “Gradient-Based Learning Applied to Document Recognition,” Proc. of the IEEE, Vol. 86, No. 11, pp. 2278–2324, 1998.

    Article  Google Scholar 

  13. Hinton, G. E., Osindero, S., and Teh, Y.-W., “A Fast Learning Algorithm for Deep Belief Nets,” Neural Computation, Vol. 18, No. 7, pp. 1527–1554, 2006.

    Article  MathSciNet  MATH  Google Scholar 

  14. Collobert, R. and Weston, J., “A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning,” Proc. of the 25th ICML, Vol. 25, pp. 160–167, 2008.

    Google Scholar 

  15. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-R., et al., “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Processing Magazine, Vol. 29, No. 6, pp. 82–97, 2012.

    Article  Google Scholar 

  16. Sonka, M., Hlavac, V., and Boyle, R., “Image Processing, Analysis, and Machine Vision,” Cengage Learning, pp. 407–409, 2014.

    Google Scholar 

  17. Soukup, D. and Huber-Mork, R., “Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images,” in: Advanced is Visual Computing, George, B., Richard, B., (Eds.), Springer, pp. 668–677, 2014.

    Google Scholar 

  18. Yao, S. and Hai-Ru, L., “Detection of Weft Knitting Fabric Defects Based on Windowed Texture Information and Threshold Segmentation by CNN,” Proc. of IEEE on Digital Image Processing, pp. 292–296, 2009.

    Google Scholar 

  19. Kwon, B.-K., Won, J.-S., and Kang, D.-J., “Fast Defect Detection for Various Types of Surfaces Using Random Forest with VOV Features,” Int. J. Precis. Eng. Manuf., Vol. 16, No. 5, pp. 965–970, 2015.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Dong-Joong Kang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, JK., Kwon, BK., Park, JH. et al. Machine learning-based imaging system for surface defect inspection. Int. J. of Precis. Eng. and Manuf.-Green Tech. 3, 303–310 (2016).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: