MPLNet: Industrial Anomaly Detection with Memory Bank and Prompt Learning
Hao Zhou, Xuanru Guo, Mingcheng Ji, Jielin Jiang, Haolong Xiang, Shengjun Xue · 2024
Anomaly detection is a critical aspect of industrial production processes. Most of the self-supervised training system utilise Convolutional Neural Networks (CNNs) for feature extraction. However, these methods often exhibit poor detection performance on small structural anomalies due to the limited receptive field of CNN. The prompt learning method has been demonstrated to be an effective means of enhancing the global information extraction abilities of the model. However, this approach necessitates the input of a significant number of manual prompts. To address these issues, we propose a Memory And Prompt Learning Based Network (MPLNet) for anomaly detection. MPLNet obtains the differences information between normal and detected images by comparing their features and then uses these differences to generate prompts. By automatically generating prompts, it reduces the workload of manually entering prompts in prompt learning. At the same time, it effectively enhances the model’s ability to extract and use global information. Extensive experiments have shown that the proposed MPLNet achieves state-of-the-art anomaly detection performance on the widely used and challenging MVTec AD dataset and MVTec AD-3D dataset. Index Terms—Anomaly detection, U-Net, Transformer, Prompt, artificial intelligence System