Abnormal Detection by Iterative Reconstruction

Abstract

We propose an automatic abnormal detection method using subspace and iterative reconstruction for visual inspection. In visual inspection, we obtain many normal images and little abnormal images. Thus, we use a subspace method which is trained from only normal images. We reconstruct a test image by the subspace and detect abnormal regions by robust statistics of the difference between the test and reconstructed images. However, the method sometimes gave many false positives when black artificial abnormal regions are added to white regions. This is because neighboring white regions of the black abnormity become dark to represent the black abnormity. To overcome it, we use iterative reconstruction by replacing the abnormal region detected by robust statistics into an intensity value made from normal images. In experiments, we evaluate our method using 4 machine parts and confirmed that the proposed method detect abnormal regions with high accuracy.