The intelligent detection process for industrial anomalies employs artificial intelligence methods to classify images that deviate from a normal appearance. Traditional convolutional neural network (CNN)-based anomaly detection algorithms mainly use the network to restructure abnormal areas and detect anomalies by calculating the errors between the original image and reconstructed image. However, the traditional CNNs struggle to extract global context information, resulting in poor anomaly detection performance. Thus, a masked Swin Transformer Unet (MSTUnet) for anomaly detection is proposed. To solve the problem of insufficient abnormal samples in the training phase, an anomaly simulation and mask strategy is first applied on anomaly-free samples to generate a simulated anomaly and, then, the Swin Transformer's powerful global learning ability is used to inpaint the masked area. Finally, a convolution-based Unet network is used for end-to-end anomaly detection. Experimental results on industrial dataset MVTec AD show that MSTUnet achieves superior anomaly detection and localization performance.

中文翻译：

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用于工业异常检测的 Masked Swin Transformer Unet


工业异常智能检测过程采用人工智能方法对偏离正常外观的图像进行分类。传统的基于卷积神经网络（CNN）的异常检测算法主要利用网络重构异常区域，通过计算原始图像与重构图像之间的误差来检测异常。然而，传统的 CNN 难以提取全局上下文信息，导致异常检测性能较差。因此，提出了一种用于异常检测的屏蔽 Swin Transformer Unet (MSTUnet)。针对训练阶段异常样本不足的问题，首先对无异常样本应用异常模拟和屏蔽策略，生成模拟异常，然后利用Swin Transformer强大的全局学习能力对屏蔽区域进行修复。最后，基于卷积的Unet网络用于端到端异常检测。在工业数据集 MVTec AD 上的实验结果表明，MSTUnet 实现了卓越的异常检测和定位性能。