PM-AE: Pyramid Memory Autoencoder for Unsupervised Textured Surface Defect Detection

Anomaly detection for textured surface is a key task in product quality control. In recent years, supervised deep learning approaches have begun to be applied in this field, whereas most of the approaches are usually impracticable in collecting a large scale of defect samples. To this end, this paper proposes an efficient pyramid memory autoencoder. A new approach is developed by reconstructing background texture using autoencoder neural networks with multi-scale spatial pyramid pooling module and affinity memory module. With only positive samples for training phase, the proposed method managed to localize various defects on textured surfaces. Experimental results demonstrated not only its superiority on efficiency and accuracy, but also the great potential in industrial applications.