Frequent false alarms impede the promotion of unsupervised anomaly detection algorithms in industrial applications. Potential characteristics of false alarms depending on the trained detector are revealed by investigating density probability distributions of prediction scores in the out-of-distribution anomaly detection tasks. An SVM-based classifier is exploited as a post-processing module to identify false alarms from the anomaly map at the object level. Besides, a sample synthesis strategy is devised to incorporate fuzzy prior knowledge on the specific application in the anomaly-free training dataset. Experimental results illustrate that the proposed method comprehensively improves the performances of two segmentation models at both image and pixel levels on two industrial applications.

翻译：频繁的误报阻碍了无监督异常检测算法在工业应用中的推广。通过分析离群分布异常检测任务中预测得分的密度概率分布，揭示了依赖于训练检测器的误报潜在特征。利用基于支持向量机的分类器作为后处理模块，在目标层级从异常图中识别误报。此外，设计了一种样本合成策略，将特定应用中的模糊先验知识融入无异常训练数据集。实验结果表明，该方法在两项工业应用中全面提升了两种分割模型在图像级和像素级的性能。