Anomaly detection of defect using energy of point pattern features within random finite set framework

In this paper, we propose a lightweight approach for industrial defect detection that is modeled based on anomaly detection using point pattern data. Most recent works use global features for feature extraction to summarize image content. However, global features are not robust against lighting and viewpoint changes and do not describe images' geometrical information which, thus, cannot be fully utilized in manufacturing industry. To the best of our knowledge, we are the first to propose using transfer learning of local/point pattern features to overcome these limitations and capture geometrical information of the interested image regions. We model these local/point pattern features as a random finite set (RFS). In addition, we propose RFS energy, in contrast to RFS likelihood as an anomaly score. The similarity distribution of point pattern features of the normal sample has been modeled as a multivariate Gaussian. Parameters learning of the proposed RFS energy does not require any heavy computation. We evaluate the proposed approach on the MVTec AD dataset, a multi-object defect detection dataset. Experimental results show the outstanding performance of our proposed approach compared to the state-of-the-art methods, and the proposed RFS energy outperforms the state-of-the-art in the few shot learning settings. Also, we evaluated the proposed approach on needle dataset, a real life application, to detect defective needle samples given three different views. The results show superior performance compared to DiffeNet.