Visual Anomaly Detection with Self-Attention and Separate Memory Bank

Declining birthrate and aging populations are progressing all over the world. This has led to labor shortage, making visual inspections more challenging in various industries. Recently, visual anomaly detection methods using deep learning have been proposed to solve these problems. However, they are computationally expensive and difficult to infer in real-time, even in a GPU environment. In addition, while they detect structural anomalies (e.g., scratches and stains), logical anomalies (e.g., mis-position and mis-number) cannot be detected. This work proposes an anomaly detection method to detect both structural and logical anomalies with high speed by improving Patch Core. The proposal applies self-attention mechanism for the intermediate layer of the pre-trained Convolutional Neural Networks(CNN) model. Self-attention mechanism enables the model to understand the relationships between image features and detect logical anomalies. In addition, the global and local features are extracted from the intermediate layer of the pretrained CNN model and stored in Separate Memory Bank (SMB). SMB leads to improving AUROC, which represents accuracy, by calculating features for each feature type. It also avoids unnecessary upsampling and reduces the dimensionality, thus improving inference speed. Experiments validate the proposed method and compare previous anomaly detection methods. Experiments evaluate the performance of the proposal for the CAD-SD dataset and MVTec LOCO dataset, which contains structural and logical anomalies. For Co-occurrence dataset, the experimental results show that the proposal achieves 98.5% (improving 2.2%) for AUROC and 16.1 (improving 66.6%) for FPS compared to the state-of-the-art method. Also, the experimental results show that the proposal achieves 82.8% (improving 0.9%) for MVTec-LOCO dataset. Hence, the proposal can contribute to the efficiency and automation of manufacturing, medical, and other fields.