In the paper, it is proposed to develop a machine learning based intelligent defect 10 detection system for metal products. The common machine vision system has the surface (stain, 11 shallow pit, shallow tumor, scratches, Edge defects, pattern defects) detection, or for the processing 12 of the size, diameter, diameter, eccentricity, height, thickness and other parts of the non-contact 13 numerical parameters of detection. Considering the quality of the work piece and the defects of the 14

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... dard, so for the quality of customized testing requirements, the study is the development of 15 machine vision and machine learning metal products defect detection system, mainly composed of 16 three procedures: Image preprocessing, training procedures and testing procedures. The system 17 architecture consists of three parts: (1) Image preprocessing: we first use the machine vision. 18 OPENCV to carry out the image pre-processing part of the product before the detection. (2) 19 Training procedures: The algorithm of the machine learning includes the convolution neural 20 network (CNN), chunk-max pooling is used to train the program, and the generative adversarial 21 network (GAN) based architecture is used to solve the problem of small datasets for surface defects. 22 33