Anomaly Detection for Industrial Inspection using Convolutional Autoencoder and Deep Feature-based One-class Classification

Abstract

Part-to-part and image-to-image variability pose a great challenge to automatic anomaly detection systems; an additional challenge is applying deep learning methods on high-resolution images. Motivated by these challenges together with the promising results of transfer learning for anomaly detection, this paper presents a new approach combing the autoencoder-based method with one class deep feature classification. Specifically, after training an autoencoder using only normal images, we compute error images or anomaly maps between input and reconstructed images from the autoencoder. Then, we embed these anomaly maps using a pre-trained convolutional neural network feature extractor. Having the embeddings from the anomaly maps of training samples, we train a one-class classifier, k nearest neighbor, to compute an anomaly score for an unseen sample. Finally, a simple threshold-based criterion is used to determine if the unseen sample is anomalous or not. We compare the proposed algorithm with state-of-the-art methods on multiple challenging datasets: one representing zipper cursors, acquired specifically for this work; and eight belonging to the recently introduced MVTec dataset collection, representing various industrial anomaly detection tasks. We find that the proposed approach outperforms alternatives in all cases, and we achieve the average precision score of 94.77% and 96.35% for zipper cursors and MVTec datasets on average, respectively.