  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.
