Class-agnostic adaptive feature adaptation method for anomaly detection of aero-engine blade

Abstract

Regular borescope inspection of aero-engine blades is crucial to ensure the safe operation of the aero-engine. To address the problem of unavailable defective blade images, this paper focuses on the intelligent borescope inspection method based on anomaly detection. Previous anomaly detection methods rely on the features pre-trained on the natural images. Since there is a large domain gap between natural images and blade images, the discriminativeness of pre-trained features is suboptimal. To alleviate this problem, current methods adapt the pre-trained features based on the prior assumption of the class number of normal data. In real scenarios, since the class number of normal data is commonly unknown, previous adaptation methods fail in some cases. In this paper, we propose a class-agnostic feature adaptation method (${\mathrm{CA}}^2$) to solve the above problem. The key insight is to utilize the neighbor relationship of each pre-trained feature to adaptively cluster towards the center of the k nearest neighbor samples. We conduct the experiment under multiple known classes. The results show that ${\mathrm{CA}}^2$ achieves a consistent improvement under different class numbers of normal data. The engineering experiment on anomaly detection of aero-engine blades shows a decent anomaly detection performance of ${\mathrm{CA}}^2$. Code and dataset are available at https://github.com/changniu54/CA².