An Anomaly Feature-Editing-Based Adversarial Network for Texture Defect Visual Inspection

Abstract

Establishing a unified model for the defect inspection of different texture surfaces remains a challenge in the industrial automation field because these surfaces can vary in regular and irregular ways. Current unsupervised learning methods are trained on defect-free samples only and cannot directly address anomalies during testing, which precludes these methods from simultaneously inspecting for various texture defects. In this article, we propose a novel unsupervised anomaly feature-editing-based adversarial network (AFEAN) to accurately inspect various texture defects. To impart the AFEAN with the ability to address anomalies, a paired input, consisting of a defect-free image and an artificially defective image, is utilized for training. First, the AFEAN employs a feature extraction module (FEM) to extract latent features for the paired input. Subsequently, a novel anomaly feature detection module (AFDM) is proposed to detect anomaly features of the artificially defective image in the latent space. In the proposed AFDM, a novel central-constraint-based clustering method is proposed to detect anomaly features by learning the distribution of the latent features. Next, a novel global context feature editing module (GCFEM) is proposed to convert the detected anomaly features to normal features to suppress the reconstruction of defects. Finally, a feature decoding module (FDM) utilizes the edited features to reconstruct the texture background. Through the AFDM and GCFEM, the AFEAN achieves the ability to address anomaly features, effectively suppressing the reconstruction of defects on the texture background. In addition, to further improve the texture reconstruction accuracy, a pixel-level discrimination module (PDM) is employed to reconstruct texture details. In the testing phase, the defects are segmented by the residual image between the input image and the reconstructed texture background. The extensive experimental results demonstrate that the AFEAN achieves the state-of-the-art inspection accuracy.