Anomaly detection and localization are important problems in computer vision. Recently, Convolutional Neural Network (CNN) has been used for visual inspection. In particular, the scarcity of anomalous samples increases the difficulty of this task, and unsupervised leaning based methods are attracting attention. We focus on Student-Teacher Feature Pyramid Matching (STPM) which can be trained from only normal images with small number of epochs. Here we proposed a powerful method which compensates for the shortcomings of STPM. Proposed method consists of two students and two teachers that a pair of student-teacher network is the same as STPM. The other student-teacher network has a role to reconstruct the features of normal products. By reconstructing the features of normal products from an abnormal image, it is possible to detect abnormalities with higher accuracy by taking the difference between them. The new student-teacher network uses attention modules and different teacher network from the original STPM. Attention mechanism acts to successfully reconstruct the normal regions in an input image. Different teacher network prevents looking at the same regions as the original STPM. Six anomaly maps obtained from the two student-teacher networks are used to calculate the final anomaly map. Student-teacher network for reconstructing features improved AUC scores for pixel level and image level in comparison with the original STPM.
翻译:异常的检测和本地化是计算机视觉中的重要问题。最近,在视觉检查中使用了革命神经网络(CNN ) 。特别是,异常抽样的缺乏增加了这项任务的难度,而未经监督的倾斜方法正在引起注意。我们注重学生-教师特色的功能性图象匹配(STPM ),这种匹配只能从仅有少量时代的普通图像中进行训练。我们在这里建议了一个强有力的方法来弥补STPM的缺陷。提议的方法包括两名学生和两名教师,他们的师生网络与STPM相同。其他学生-教师网络的作用是重建正常产品的特点。通过从不正常的图像中重建正常产品的特征,有可能通过利用它们之间的差异来发现更精确的异常。新的学生-教师网络使用关注模块和最初STPM的教师网络的不同网络。注意机制是为了成功地重建正常区域输入图像。不同的教师网络无法与STPM 相同的区域,从最初的STPM 重建学生- StA级图像网络中获取的六份异常图象图象图,用来对学生-SBA级的校际图比。