Learning Multiresolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces

Abstract

In industrial quality assessment, monitoring whether the textured product contains defects is a critical step. Compared to a large number of defect-free images that are easy to obtain, anomaly samples are limited and vary randomly in size and type. It is challenging to develop an automatic and accurate texture defect localization system that only uses normal images for training. In this paper, a multi-resolution feature learning network is proposed to detect various texture defects in an unsupervised manner. A robust pre-trained model is first employed to extract the perceptual features from the input image, then the perceptual features of various layers are fed to the corresponding multi-scale autoencoder framework. This hierarchical alignment strategy aids in receiving multi-level information for locating anomalies of various sizes. Moreover, a residual attention module (RAM) is embedded in the architecture to further improve the detection performance. Our proposed method has achieved state-of-the-art performance on the texture dataset of MVTecAD. We also extended the experiment to the real industrial texture datasets, and its detection result is better than the major existing advanced techniques.