Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns; a multisize self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN.
翻译:在工业制造业中,为了提高效率和有效性,异常探测和本地化被广泛使用;异常是罕见的,很难收集和监督模型,难以轻易地与这些观察到的异常情况相比,因为有少量异常样品,产生不令人满意的性能;另一方面,异常现象一般是微妙的,难以辨别,而且有各种外观,因此难以发现异常现象,更不用说定位异常区域;为解决这些问题,我们提议了一个称为Protomodic残余网络的框架,它了解异常现象和正常模式之间不同规模和大小的残余特征,以准确重建异常区域分解图。 PRON主要由两部分组成:明确代表异常现象对正常模式的残余特征的多尺度原型;多尺寸的自我注意机制,使不同大小的异常特征学习变得困难。此外,我们提出了各种各样的异常生成战略,既考虑到可见的和看不见的外观差异,以扩大异常现象并使其多样化。关于具有挑战性和广泛使用的MVTec AD基准的广泛实验显示,PRN比目前状态的不甚完善状态图样图,又展示了SOTA的附加结果。