Deep Anomaly Detection via Morphological Transformations
Taehyeon Kim, Yoonsik Choe · 2020
The goal of deep anomaly detection is to identify abnormal data by utilizing a deep neural network trained by a normal training dataset. In general, industrial visual anomaly detection problems distinguish normal and abnormal data through small morphological differences, such as cracks and stains. Nevertheless, most existing algorithms focus on capturing not morphological features, but semantic features of normal data. Therefore, they yield poor performance on real-world visual inspection, even though they show their superiority in simulations with representative image classification datasets. To solve this problem, we propose a novel deep anomaly detection method that encourages understanding of salient morphological features of normal data. The main idea behind our algorithm is to train a multi-class model to classify between dozens of morphological transformations applied to all the given data. To this end, the proposed algorithm utilizes a self-supervised learning strategy, which makes unsupervised learning straightforward. Additionally, we present a kernel size loss to enhance the proposed neural networks’ morphological feature representation power. This objective function is defined as the loss between predicted kernel size and label kernel size via morphologically transformed images with the label kernel. In all experiments on the industrial dataset, the proposed method demonstrates superior performance. For instance, in the MVTec anomaly detection task, our model achieved an area under the receiver operating characteristic (AUROC) value of 72.92%, which is 8.74% higher than the semantic-feature-based deep anomaly detection.