In the realm of machine learning, the study of anomaly detection and localization within image data has gained substantial traction, particularly for practical applications such as industrial defect detection. While the majority of existing methods predominantly use Convolutional Neural Networks (CNN) as their primary network architecture, we introduce a novel approach based on the Transformer backbone network. Our method employs a two-stage incremental learning strategy. During the first stage, we train a Masked Autoencoder (MAE) model solely on normal images. In the subsequent stage, we apply pixel-level data augmentation techniques to generate corrupted normal images and their corresponding pixel labels. This process allows the model to learn how to repair corrupted regions and classify the status of each pixel. Ultimately, the model generates a pixel reconstruction error matrix and a pixel anomaly probability matrix. These matrices are then combined to produce an anomaly scoring matrix that effectively detects abnormal regions. When benchmarked against several state-of-the-art CNN-based methods, our approach exhibits superior performance on the MVTec AD dataset, achieving an impressive 97.6% AUC.

翻译：在机器学习领域，图像数据中的异常检测与定位研究已获得显著关注，尤其在工业缺陷检测等实际应用中。现有方法大多以卷积神经网络（CNN）作为主要网络架构，而我们提出了一种基于Transformer主干网络的新方法。该方法采用两阶段增量学习策略：第一阶段，我们仅使用正常图像训练掩码自编码器（MAE）模型；第二阶段，应用像素级数据增强技术生成受损的正常图像及其对应的像素标签，使模型学习修复受损区域并分类每个像素的状态。最终，模型生成像素重建误差矩阵和像素异常概率矩阵，两者相结合形成异常评分矩阵，从而有效检测异常区域。与多种最先进的CNN方法相比，我们的方法在MVTec AD数据集上展现出卓越性能，实现了97.6%的AUC值。