Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework

TL;DR

This paper evaluates the effectiveness of point pattern features like SIFT within the random finite set framework for defect detection in manufacturing, showing improved accuracy over existing methods.

Contribution

It introduces a novel evaluation of point pattern features within the random finite set framework for anomaly detection in manufacturing defect inspection.

Findings

SIFT features improve defect detection accuracy.

Random finite set methods outperform some state-of-the-art anomaly detection techniques.

Deep features also evaluated but SIFT shows most consistent results.

Abstract

Defect detection in the manufacturing industry is of utmost importance for product quality inspection. Recently, optical defect detection has been investigated as an anomaly detection using different deep learning methods. However, the recent works do not explore the use of point pattern features, such as SIFT for anomaly detection using the recently developed set-based methods. In this paper, we present an evaluation of different point pattern feature detectors and descriptors for defect detection application. The evaluation is performed within the random finite set framework. Handcrafted point pattern features, such as SIFT as well as deep features are used in this evaluation. Random finite set-based defect detection is compared with state-of-the-arts anomaly detection methods. The results show that using point pattern features, such as SIFT as data points for random finite set-based anomaly detection achieves the most consistent defect detection accuracy on the MVTec-AD dataset.