  Recent advances in the industrial inspection of textured surfaces-in the form
of visual inspection-have made such inspections possible for efficient,
flexible manufacturing systems. 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--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.
