Abstract: The mechanization of industrial production puts forward new requirements for the inspection of industrial product quality,and a high-precision,easy-to-transplantanomaly detection algorithm is required to adapt to the update of production methods.Aiming at the inherent problem of low probability of abnormal samples in industrial production and incomplete prediction,a generative industrial anomaly localization model based on fuzzy masking and dynamic reasoning is proposed.Firstly,a contrast sample generation module based on random fuzzy occlusion is designed to obtain high-quality simulated anomalous images.At the same time,the shallow feature fusion path is used to retain more edge information,the loss-loss function weighting is used to make the model pay more attention to structural similarity,and the contrast learning method is used to make the network obtain better representation ability.Secondly,in order to alleviate the problem of blurred output images of generative models,a multi-branch anomaly dynamic inference method is designed,and the two branches of iterative generation and accurate repair cooperate with each other to widen the distance between background noise and real anomalies.Experimental results show that the proposed method achieves an average localization accuracy of 91.42% on the MVTec dataset,and the top three anomalous localization accuracy are obtained in 12 classes.The location of anomalies can be obtained more completely.For images with complex textures and large backgrounds,it still maintains high index sensitivity,and the average anomaly localization performance has reached the best in published generative detection models published in recent years.

Key words: Industrial image detection, Anomaly location, Deep learning, Generative learning, Dynamic anomaly inference