Abstract: Anomaly detection on the surface of industrial products is an indispensable link in manufacturing.In actual industrial production,there are common phenomena such as low proportion of abnormal samples and complex and changeable unknown abnormal,which in turn cause a series of negative effects such as overfitting and poor generalization ability on few-shot datasets.In recent years,the idea of normalized flow has brought a new approach to the field of industrial image anomaly detection based on deep learning,but the inherent architecture of normalized flow easily leads to insufficient model expressiveness.Aiming at the above difficulties,a continuous dense normalized flow model for industrial image anomaly detection is proposed.First,a feature extraction network pre-training strategy based on contrastive learning is designed,which involves simulated abnormal data and a small amount of real abnormal data in the contrastive learning task,and trains the feature backbone network AlexNet to narrow or widen the distance between specific samples.Secondly,a continuous dense normalized flow model is designed,and it uses a composite architecture of reversible transformation to construct a dense flow module to enhance the fitting ability of the generative model to the distribution.The experimental datasets include MVTec AD,Magnetic Tile Defects and self-made industrial cloth datasets.Compared with other anomaly detection models,our method achieves optimal or sub-optimal detection performance on the three datasets,respectively.

Key words: Industrial image detection, Anomaly detection, Deep learning, Normalized flow, Contrastive learning