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

Ammar Mansoor Kamoona, Amirali Khodadadian Gostar, Xiaoying Wang, Mark Easton, Alireza Bab-Hadiashar, Reza Hoseinnezhad

Abstract: Highlights•A lightweight approach for defect detection based on anomaly detection is proposed.•Transfer learning of deep local features is used instead of global features.•We model these local/point pattern features as a random finite set (RFS).•We propose RFS energy, in contrast to RFS likelihood as an anomaly score.•Results show the outstanding performance of our proposed approach compared to the state-of-the-art methods in few-shot learning.