Due to the extreme imbalance in the number of normal data and abnormal data, visual anomaly detection is important for the development of industrial automatic product quality inspection. Unsupervised methods based on reconstruction and embedding have been widely studied for anomaly detection, of which reconstruction-based methods are the most popular. However, establishing a unified model for textured surface defect detection remains a challenge because these surfaces can vary in homogeneous and non regularly ways. Furthermore, existing reconstruction-based methods do not have a strong ability to convert the defect feature to the normal feature. To address these challenges, we propose a novel unsupervised reference-based autoencoder (RB-AE) to accurately inspect a variety of textured defects. Unlike most reconstruction-based methods, artificial defects and a novel pixel-level discrimination loss function are utilized for training to enable the model to obtain pixel-level discrimination ability. First, the RB-AE employs an encoding module to extract multi-scale features of the textured surface. Subsequently, a novel reference-based attention module (RBAM) is proposed to convert the defect features to normal features to suppress the reconstruction of defects. In addition, RBAM can also effectively suppress the defective feature residual caused by skip-connection. Next, a decoding module utilizes the repaired features to reconstruct the normal texture background. Finally, a novel multiscale feature discrimination module (MSFDM) is employed to defect detection and segmentation.

翻译：由于正常数据和异常数据数量极不平衡,目视异常现象的探测对于发展工业自动产品质量检查十分重要,在重建和嵌入的基础上,广泛研究了未受监督的方法,以发现异常现象,其中以重建为基础的方法最受欢迎;然而,为纹理表面缺陷检测建立一个统一的模型仍是一项挑战,因为这些表面可能以同质和非定期的方式出现差异;此外,基于重建的现有方法没有很强的能力将缺陷特征转换成正常特征。为了应对这些挑战,我们提议采用新的、未经监督的自动自动调试器(RB-AE)来准确检查各种发质缺陷。与大多数基于重建的方法不同,人工缺陷和新的像素级歧视损失功能被用于培训,以使模型获得像素级歧视能力。首先,基于重建的现有方法使用一个编码模块来提取纹理表面的多级特征。随后,我们提议采用一个新的基于参考的注意模块(RBAM)来将缺陷特征转换为正常特征,以抑制缺陷的特性。此外,通过升级的SBAM模型将正常的变压性升级的变压性模型用于升级的变压。