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The application of convolutional neural network (CNN) was proposed in workpiece defect detection, which can detect workpiece defects on its surface, to improve the product quality. The CNN can t̂ recognize the small defects of a product although it can classify different objects very well. This paper presented a method which uses CNN for de­ fect detection based on the results of a recognition process. Firstly the defective samples are expanded to overcome the difficulty for lacking of training samples. Then by observing the output data obtained from a recognition CNN?a concept called “the defect distinguish ratio” is defined to measure the degree of defection. It is considered as a non-defect pattern only when the defect distinguish ratio reaches a certain level. Finally, the experiment demonstrates the validity and feasi­ bility of the method,in which the defect detection ratio can reach 93. 3 % .

基于CNN的工件缺陷检测方法研究 (Research of Workpiece Defect Detection Method Based on CNN).