Multi-Category Decomposition Editing Network for the Accurate Visual Inspection of Texture Defects

Abstract

Spotting blemished areas automatically on a textured surface is a particular challenge, as both nominal and defective surface samples are inconsistent in large-scale industrial manufacturing. The most efficient solution uses the memory bank extracted from the nominal samples to detect outliers. We approach our strategy, the multi-category decomposition editing network (MCDEN), from a similar viewpoint. Notably, we do not use defect-free samples. Instead, we use virtual results to construct a defect library. MCDEN decomposes abnormalities to basic elements from the library while editing outlier features to reconstruct the texture normality, offering a rational segmentation map through decomposition and reconstruction. Based on this strategy, MCDEN is more interpretable than most neural network methods since interpretability is particularly important in industrial production to ensure stability; however, the existing deep learning methods are similar to a black box structure, which makes MCDEN more appropriate in industry. Experiments on texture surface samples from the MVTec anomaly detection (MVTAD) dataset confirm the efficacy of MCDEN with a pixel-level area under the receiver operator characteristic curve (AUC) score of 96.6%. In other experiments collected from semi-manufactured inkjet printing organic electroluminescence display (OLED) panels, MCDEN demonstrated competitive results with a 99.2% detection rate and rapid real-time detection capability <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —MCDEN detects defects based on the assumption that texture defects are decomposed into five basic transformation combinations. The method does not need to collect additional defect images for model training. It only needs to collect about 2000 positive defect-free images to generate negative samples through the random generation of defects and complete the training. All training images and detection images are based on a single-channel gray image. The MCDEN method can be applied to the detection of object defects on textured surfaces with periodic features, such as steel, leather, and screen. Based on the known texture features, the trained model has high detection accuracy and real-time detection for specific types of textured surfaces.

Topics & Concepts