PUSHING THE LIMITS OF FEW-SHOT ANOMALY DE-TECTION IN INDUSTRY VISION: GRAPHCORE

Abstract

In the area of few-shot anomaly detection (FSAD), efficient visual feature plays an essential role in the memory bank M-based methods. However, these methods do not account for the relationship between the visual feature and its rotated visual feature, drastically limiting the anomaly detection performance. To push the limits, we reveal that rotation-invariant feature property has a significant impact on industrial-based FSAD. Specifically, we utilize graph representation in FSAD and provide a novel visual isometric invariant feature (VIIF) as an anomaly measurement feature. As a result, VIIF can robustly improve the anomaly discriminating ability and can further reduce the size of redundant features stored in M by a large amount. Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and improve the performance of anomaly detection. A comprehensive evaluation is provided for comparing GraphCore and other SOTA anomaly detection models under our proposed few-shot anomaly detection setting, which shows GraphCore can increase average AUC by 5.8%, 4.1%, 3.4%, and 1.6% on MVTec AD and by 25.5%, 22.0%, 16.9%, and 14.1% on MPDD for 1, 2, 4, and 8-shot cases, respectively.

1. INTRODUCTION

With the rapid development of deep vision detection technology in artificial intelligence, detecting anomalies/defects on the surface of industrial products has received unprecedented attention. Changeover in manufacturing refers to converting a line or machine from processing one product to another. Since the equipment has not been completely fine-tuned after the start of the production line, changeover frequently results in unsatisfactory anomaly detection (AD) performance. How to achieve rapid training of industrial product models in the changeover scenario while assuring accurate anomaly detection is a critical issue in the actual production process. The current state of AD in the industry is as follows: (1) In terms of detection accuracy, the performance of state-ofthe-art (SOTA) AD models degrades dramatically during the changeover. Current mainstream work utilizes a considerable amount of training data as input to train the model, as shown in Fig. 1(a) . However, this will make data collecting challenging, even for unsupervised learning. As a result, many approaches based on few-shot learning at the price of accuracy have been proposed. For instance, Huang et al. ( 2022) employ meta-learning, as shown in Fig. 1(b) . While due to complicated settings, it is impossible to migrate to the new product during the changeover flexibly, and the detection accuracy cannot be guaranteed. (2) In terms of training speed, when a large amount of data is utilized for training, the training progress for new goods is slowed in the actual production line. As is well-known, vanilla unsupervised AD requires to collect a large amount of information. Even though meta-learning works in few-shot learning, as shown in Fig. 1 (b), it is still necessary to train a massive portion of previously collected data.

