Machine Vision Detection Method for Surface Defects of Automobile Stamping Parts

Authors

  • Lei Geng School of Electronic and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • YuXiang Wen School of Electronic and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • Fang Zhang School of Electronic and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • YanBei Liu School of Electronic and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China

Keywords:

Surface defect detection, Machine vision, Convolutional neural network, Camera calibration, Image segmentation.

Abstract

A new system for detecting surface defects of automobile stamping parts is designed. Set up a defect detection system, which includes light source, camera and lens. Through this system, auto body surface image is collected, defect data set is established and manual annotation is performed. The marked training set was used to train the convolutional neural network, and then the convolutional neural network was used to segment the surface defects at the pixel level to obtain the defect regions of different categories. The experimental results show that the surface defect detection method proposed in this paper can effectively segment the surface defects of the workpiece, and the accuracy of scratch and rust segmentation is high.

References

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Published

2019-03-04

How to Cite

Geng, L., Wen, Y., Zhang, F., & Liu, Y. (2019). Machine Vision Detection Method for Surface Defects of Automobile Stamping Parts. American Scientific Research Journal for Engineering, Technology, and Sciences, 53(1), 128–144. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/4734

Issue

Vol. 53 No. 1 (2019)

Section

Articles

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