Defect detection aims to detect and localize regions out of the normal distribution.Previous approaches model normality and compare it with the input to identify defective regions, potentially limiting their generalizability.This paper proposes a one-stage framework that detects defective patterns directly without the modeling process.This ability is adopted through the joint efforts of three parties: a generative adversarial network (GAN), a newly proposed scaled pattern loss, and a dynamic masked cycle-consistent auxiliary network. Explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping.Experimental results on the texture class of the challenging MVTec AD dataset show that the proposed method is 2.9% higher than the SOTA methods in F1-Score, while substantially outperforming SOTA methods in generalizability.

翻译：缺陷检测旨在检测并定位偏离正态分布的区域。现有方法通过建模正态分布并与输入比较来识别缺陷区域，这限制了其泛化能力。本文提出一种无需建模过程即可直接检测缺陷模式的一阶段框架。该能力通过三部分协同实现：生成对抗网络（GAN）、新提出的缩放模式损失函数，以及动态掩膜循环一致性辅助网络。实验刻意排除了可能指示缺陷位置的显式信息，以避免学习任何直接映射。在MVTec AD数据集的纹理类挑战性任务中，所提方法在F1分数上较当前最优方法提升2.9%，且泛化能力显著优于现有最优方法。