Machine Vision Detection Method for Surface Defects of Automobile Stamping Parts

Lei Geng, YuXiang Wen, Fang Zhang, YanBei Liu


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.


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

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