自动化与信息工程2024，Vol.45Issue(4)：30-35,6.DOI:10.3969/j.issn.1674-2605.2024.04.005
改进掩码自编码器的多类工业图像异常检测方法
Improved Mask Autoencoder for Multi-class Industrial Image Anomaly Detection Method
摘要
Abstract
Aiming at the differences between different abnormal image data and the insufficient generalization ability of deep models,an improved mask autoencoder based multi class industrial image anomaly detection method is proposed.Firstly,an improved Mask Autoencoder(MAE)model is trained using normal image sample data to enable the model to reconstruct normal images;Then,based on the difference between the reconstructed image of the improved MAE model and the original image,distinguish between normal and abnormal image data;Finally,this method was used to simultaneously detect multiple categories of abnormal image data on a publicly available industrial image dataset,with an average AUC of 0.895.Compared with MKD,U-Net,and DAGAN,the detection accuracy was improved by 2.05%,9.28%,and 2.52%,respectively,verifying the effectiveness of this method.关键词
编解码重构/掩码自编码器/异常检测/工业图像Key words
encoder-decoder reconstruction/masked autoencoder/anomaly detection/industrial image分类
信息技术与安全科学引用本文复制引用
胡洋,肖明,康嘉文..改进掩码自编码器的多类工业图像异常检测方法[J].自动化与信息工程,2024,45(4):30-35,6.基金项目
广东省科技公关计划(2022A1515011445) （2022A1515011445）
大范围场景空间定位与自然人机交互关键技术(502200027). （502200027）
