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,对于图像数据中的异常检测和定位的研究引起了大量的关注,特别是在实际应用中,例如工业缺陷检测。虽然现有的方法主要依赖于卷积神经网络(CNN)作为他们的骨干网络,但是我们提出了一种基于Transformer骨干网的创新方法。我们的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Masked Autoencoder (MAE)模型。随后,在第二阶段中,我们实施像素级数据增强技术来生成受损的正常图像及其对应的像素标签。这个过程使模型学习如何修复受损的区域和对每个像素的状态进行分类。最终,模型产生了一个像素重构误差矩阵和一个像素异常概率矩阵,这些矩阵合并起来创建了一个异常评分矩阵,有效地识别了异常区域。与几种最先进的基于CNN的技术相比,我们的方法在MVTec AD数据集上表现出优秀的性能,达到了97.6%的AUC。