Semi-supervised visual anomaly detection based on convolutional autoencoder and transfer learning

Abstract

Recent advances in deep neural networks have shown that reconstruction-based methods using autoencoders have potential for anomaly detection in visual inspection tasks. However, there are challenges when applying these methods to high-resolution images, such as the need for large network training and computation of anomaly scores. Autoencoder-based methods detect anomalies by comparing an input image to its reconstruction in pixel space, which can result in poor performance due to imperfect reconstruction. In this paper, we propose a method to address these challenges by using a conditional patch-based convolutional autoencoder and one-class deep feature classification. We train an autoencoder using only normal images and compute anomaly maps as the difference between the input and output of the autoencoder. We then embed these anomaly maps using a pretrained convolutional neural network feature extractor. Using the deep feature embeddings from the anomaly maps of training samples, we train a one-class classifier to compute an anomaly score for an unseen sample. A simple threshold-based criterion is used to determine if the unseen sample is anomalous or not. We compare our proposed algorithm to state-of-the-art methods on multiple challenging datasets, including a dataset of zipper cursors and eight datasets from the MVTec dataset collection. We find that our approach outperforms alternatives in all cases, achieving an average precision score of 94.77% for zipper cursors and 96.51% for MVTec datasets.