Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection

TL;DR

This paper introduces HETMM, a novel, training-free framework for industrial anomaly detection that effectively distinguishes hard-normal examples from anomalies, achieving high accuracy and real-time speed.

Contribution

The paper proposes HETMM with ATMM and PTS, enhancing anomaly detection by handling affine transformations and hard-normal examples efficiently.

Findings

HETMM outperforms state-of-the-art methods in accuracy.

Achieves real-time inference at 26.1 FPS.

Uses a tiny template set with comparable performance.

Abstract

Anomaly detectors are widely used in industrial manufacturing to detect and localize unknown defects in query images. These detectors are trained on anomaly-free samples and have successfully distinguished anomalies from most normal samples. However, hard-normal examples are scattered and far apart from most normal samples, and thus they are often mistaken for anomalies by existing methods. To address this issue, we propose Hard-normal Example-aware Template Mutual Matching (HETMM), an efficient framework to build a robust prototype-based decision boundary. Specifically, HETMM employs the proposed Affine-invariant Template Mutual Matching (ATMM) to mitigate the affection brought by the affine transformations and easy-normal examples. By mutually matching the pixel-level prototypes within the patch-level search spaces between query and template set, ATMM can accurately distinguish between hard-normal examples and anomalies, achieving low false-positive and missed-detection rates. In addition, we also propose PTS to compress the original template set for speed-up. PTS selects cluster centres and hard-normal examples to preserve the original decision boundary, allowing this tiny set to achieve comparable performance to the original one. Extensive experiments demonstrate that HETMM outperforms state-of-the-art methods, while using a 60-sheet tiny set can achieve competitive performance and real-time inference speed (around 26.1 FPS) on a Quadro 8000 RTX GPU. HETMM is training-free and can be hot-updated by directly inserting novel samples into the template set, which can promptly address some incremental learning issues in industrial manufacturing.