Self-supervised Defect Detection Method Based on Poisson Image Fusion
In this paper,a self-supervised defect detection method based on Poisson image fusion is investigated,Poisson image fusion is used to augment the data of unlabeled normal samples to generate diversified and more realistic simulated defect samples.This method solves the problem of the small number of defect samples and not easy to label.A CANet network is proposed by combining the characteristics of defective samples,and a convolutional attention module is introduced to optimize the encoder-decoder structure,in order to prevent information loss in the sampling process.At the same time,a masked convolutional layer is added to improve the reconstruction accuracy of the input data at the end of the network.The experimental results on the MV Tec dataset achieved an overall detection AUROC of 96.1%,and the comparison with three typical detection methods further proves the effectiveness of our method with better generalization,which can meet the requirements of surface defect detection for different kinds of products in industrial production.
NETL
NSTL
万方数据
维普
