Apnet: Generating Precise Anomaly Prior Information for Mixed-Supervised Defect Detection

Mixed-supervised defect detection is an emerging paradigm, referring to using defect location information provided by unsupervised framework to enhance supervised defect detection. However, current research on unsupervised defect detection methods is limited to the MVTEC AD dataset and performs poorly on industrial images with simple structures. To address this challenge, we propose a general anomaly detection model called Anomaly Prior Network (APNet). APNet is composed of Repair Module and Discriminate Module, the Repair Module repairs defect texture into defect-free texture by vector quantization algorithm, and the Discriminate Module achieves precise anomaly localization by discovering the differences between original and repaired image on multi-scales features. In addition, we have introduced a large-scale mixed-supervised industrial dataset named the Inductance Core Defect (ICD) dataset, which consists of 20913 low-resolution (400×320) inductance core samples collected from a real pipeline. Extensive experiments conducted on ICD and MVTEC AD datasets verify the effectiveness of the proposed method compared to other advanced methods.