Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection
Qian Wan, Liang Gao, Xinyu Li
Abstract
Image Anomaly Detection is a significant stage for visual quality inspection in intelligent manufacturing systems. According to the assumption that only normal images are available during the training stage, unsupervised methods have been studied recently for image anomaly detection. But anomalous images of small scale can be collected for training in many real-world industrial scenarios, and the unsupervised methods make no use of them to improve the detection accuracy. This leads to a semi-supervised image anomaly detection with an unbalanced detection challenge. In this paper, a Logit Inducing with Abnormality Capturing (LIAC) method is proposed to address semi-supervised image anomaly detection. Firstly, a Logit Inducing Loss is proposed to train a classifier for dealing with unbalanced detection. And secondly, an Abnormality Capturing Module is proposed to address anomaly detection. With labeling only 40 anomalous images for training, the proposed LIAC method achieves a 98.8% f1-score on the image anomaly detection of the printed circuit board, compared with the state-of-the-art methods. More, the proposed LIAC method is experimentally compared with the state-of-the-art methods on MTD, ROCT, and ELPV three open-source datasets, respectively achieves f1-score of 85.2%, 96.8%, and 66.6% with given 40 anomalous images for training.