Point Pattern Feature-Based Anomaly Detection for Manufacturing Defects, in the Random Finite Set Framework

Defect detection in the manufacturing industry is of utmost importance for product quality inspection. Recently, optical defect detection has been investigated as
anomaly detection
using different deep learning methods. Most current works employ feature extraction methods that describe the entire image using a single feature vector, called the global feature. However, the use of the global feature is affected by changes in several factors, such as lighting and viewpoint changes. An alternative is to use
point pattern
features known as local features or
keypoints
which are robust to changes in conditions mentioned earlier. The use of robust point pattern features, such as SIFT, for defect detection and modelling these features within the developed random set-based method is not yet explored. This paper proposes the use of point pattern features within a random finite set framework for defect detection. Also, we evaluate different point pattern feature detectors and descriptors, handcrafted point pattern features (e.g., SIFT), and pre-trained deep features, for defect detection applications. Experiments on a large-scale defect detection dataset (MVTec-AD) are carried out. The results are compared with state-of-the-art global feature-based anomaly detection methods. Results show that using point pattern features as data points within the random finite set-based anomaly detection, achieves the most consistent defect detection accuracy on the MVTec-AD dataset. In addition, this evaluation shows that transfer learning of deep local features has promising results for defect detection.