In the machine learning domain, research on anomaly detection and localization within image data has garnered significant attention, particularly in practical applications such as industrial defect detection. While existing approaches predominantly rely on Convolutional Neural Networks (CNN) as their backbone network, we propose an innovative method based on the Transformer backbone network. Our approach employs a two-stage incremental learning strategy. In the first stage, we train a Masked Autoencoder (MAE) model exclusively on normal images. Subsequently, in the second stage, we implement pixel-level data augmentation techniques to generate corrupted normal images and their corresponding pixel labels. This process enables the model to learn how to repair corrupted regions and classify the state of each pixel. Ultimately, the model produces a pixel reconstruction error matrix and a pixel anomaly probability matrix, which are combined to create an anomaly scoring matrix that effectively identifies abnormal regions. When compared to several state-of-the-art CNN-based techniques, our method demonstrates 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引起广泛关注，尤其在工业缺陷检测等实际应用中。尽管现有方法主要采用卷积神经网络作为骨干网络，我们提出了一种基于Transformer骨干网络的创新方法。该方法采用两阶段增量学习策略。第一阶段，我们仅在正常图像上训练掩码自编码器模型。随后第二阶段，通过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，二者融合形成异常评分矩阵，从而有效识别异常区域。与多种基于CNN的最新方法相比，我们的方法在MVTec AD数据集上展现出更优性能，实现了97.6%的AUC值。