Design of product visual defect detection system based on computer vision with AI

To ensure product quality, it is necessary to detect the appearance defects of products to ensure that high-quality products enter the market. Machine deep learning technology is used to extract product defect characteristics, and parameters are adjusted through small sample detection to realize the identification training of the detection system. AI algorithm is used to conduct real-time defect positioning on the production line, which can meet the requirements of product defect detection, to control the production line for correct distribution. In the production process, with the increase of defect samples, the defect samples produced in the production are directly annotated and retrained, which can gradually improve the detection accuracy of the network model and enhance the generalization performance of the network model. After the system design is completed, the production line is tested to achieve a high detection accuracy. In addition, when detecting two similar targets, we only need to change the data set and training parameters and use the transfer learning method to load the previously trained weights into the new model for training, to achieve good results.