Anomaly Detection (AD), as a critical problem, has been widely discussed. In this paper, we specialize in one specific problem, Visual Defect Detection (VDD), in many industrial applications. And in practice, defect image samples are very rare and difficult to collect. Thus, we focus on the unsupervised visual defect detection and localization tasks and propose a novel framework based on the recent score-based generative models, which synthesize the real image by iterative denoising through stochastic differential equations (SDEs). Our work is inspired by the fact that with noise injected into the original image, the defects may be changed into normal cases in the denoising process (i.e., reconstruction). First, based on the assumption that the anomalous data lie in the low probability density region of the normal data distribution, we explain a common phenomenon that occurs when reconstruction-based approaches are applied to VDD: normal pixels also change during the reconstruction process. Second, due to the differences in normal pixels between the reconstructed and original images, a time-dependent gradient value (i.e., score) of normal data distribution is utilized as a metric, rather than reconstruction loss, to gauge the defects. Third, a novel $T$ scales approach is developed to dramatically reduce the required number of iterations, accelerating the inference process. These practices allow our model to generalize VDD in an unsupervised manner while maintaining reasonably good performance. We evaluate our method on several datasets to demonstrate its effectiveness.
翻译:异常探测(AD)是一个非常严重的问题,我们对此进行了广泛的讨论。在本文中,我们专门研究一个具体问题,即许多工业应用中的视觉缺陷检测(VDD),而实际上,缺陷图像样本非常罕见,难以收集。因此,我们侧重于未经监督的视觉缺陷检测和定位任务,并基于最近的基于分数的基因化模型提出了一个新的框架,该模型通过随机差异方程(SDEs)迭接分分解,综合真实图像。我们的工作受到以下事实的启发:在原始图像中注入噪音后,缺陷可能会在去除效果(即重建)过程中变成正常案例。首先,基于假设异常图像样本数据位于正常数据分布的低概率密度区域,我们根据这种假设,我们根据基于重建的方法应用于VDDD:正常的象素在重建过程中也会发生变化。第二,由于在重建的正常像素和原始图像之间存在差异,一个依赖时间的梯度值(i.e.corde),在去除效果过程中,正常数据分布的异常值可能变成正常的比值。在正常数据分布的尺度上,在使用一种测量的比标准,而采用一种标准的比标准的比标准的比标准的比标准的比标准要用来用来测量。在不断的尺度上,在不断的比标准。在不断的尺度上,在不断的尺度上,我们的数据比标准,在不断的精确的比标准,在不断的计算。