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Recent advances in the industrial inspection of textured surfaces&#x2014;in the form of visual inspection&#x2014;have made such inspections possible for efficient, flexible manufacturing systems. However, establishing a unified manual-feature-based inspection model for homogeneous and nonregularly textured surfaces presents an enormous challenge. Furthermore, in real industrial scenarios, collecting and labeling sufficient defective samples is impracticable due to the scarcity of defects and the endless variety of defect types, thus limiting the performance of supervised deep learning methods. To address these challenges, we propose an unsupervised feature memory rearrangement network (FMR-Net) to accurately detect various textural defects simultaneously. Consistent with mainstream methods, we adopt the idea of background reconstruction; however, we innovatively utilize artificial synthetic defects to enable the model to recognize anomalies, while traditional wisdom relies only on defect-free samples. First, we employ an encoding module to obtain multiscale features of the textured surface. Subsequently, a contrastive-learning-based memory feature module (CMFM) is proposed to obtain discriminative representations and construct a normal feature memory bank in the latent space, which can be employed as a substitute for defects and fast anomaly scores at the patch level. Next, a novel global feature rearrangement module (GFRM) is proposed to further suppress the reconstruction of residual defects. Finally, a decoding module utilizes the restored features to reconstruct the normal texture background. In addition, to improve inspection performance, a two-phase training strategy is utilized for accurate defect restoration refinement, and we exploit a multimodal inspection method to achieve noise-robust defect localization. We verify our method through extensive experiments and test its practical deployment in collaborative edge&#x2013;cloud intelligent manufacturing scenarios by means of a multilevel detection method, demonstrating that FMR-Net exhibits state-of-the-art inspection accuracy and shows great potential for use in edge-computing-enabled smart industries. Note to Practitioners&#x2014;Most conventional visual inspection methods rely on supervised training and consequently require a large amount of labeled data and can detect only specific types of texture defects. In contrast, the proposed FMR-Net is a robust model for the simultaneous and accurate inspection of textured surfaces for various defects that does not require any real labeled defect samples. Furthermore, this model can also support a different fine-grained detection method that is very suitable in the edge computing paradigm. These two characteristics are both extremely important for practical industrial applications. To the best of our knowledge, this is the first unsupervised edge intelligent vision inspection framework. As such, it can provide inspiration and serve as a reference for intelligent industry.

A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing