An Anomaly Detection Method Based on Self-supervised Learning With Soft Label Assignment for Defect Visual Inspection

Recently, local-editing-based transformations are introduced in anomaly detection for defect visual inspection, which construct a pretext task with the paradigm of self-supervised learning. However, supervised information of local-editing-based transformation may be incorrect when invalid transformation occurs in the pretext task. The reason is that the conventional method to generate labels ignores the differences of images between before and after the transformation. To address this issue, we propose soft label assignment (SLA) to construct soft labels via measuring the similarity between the original and transformed images. Meanwhile, a novel self-supervised learning-based anomaly detection method is proposed for defect visual inspection, which exploits local-editing-based transformation with SLA as a pretext classification task. A convolutional neural network (CNN) is trained to extract deep features of ambiguity and irregularity by the pretext classification task. In the main task, an anomaly detection is modeled via the deep representations to estimate defects regarded as anomalies. Experimental results demonstrate the effect of SLA, and the proposed method achieves superior performance than state-of-the-art methods in terms of the receiver operating characteristic curve (AUC-ROC).