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.

翻译：以视觉检查的形式对素质表面进行工业检查的最近进展,以视觉检查的形式使这种检查成为高效、灵活的制造系统成为可能。我们提议建立一个不受监督的特征内存重新布局网络(FMR-Net),以便同时准确地发现各种质质缺陷。我们根据主流方法,采用背景重建理念;然而,我们创新地利用人工合成缺陷,使模型能够识别异常现象,而传统智慧则仅仅依靠无缺陷样本。首先,我们使用一个编码模块,以获得素质表面的多种规模特征。随后,我们提议采用一个基于对比的学习记忆特征模块(CMFM),以获得有区别的内存结构表和在潜在空间建立一个正常的特征内存库(FMR-Net-Net),以便用来在补丁一级替代缺陷和快速异常分数。我们建议采用一个新的全球特征重新布局模块(GFRRMM),以便进一步抑制残余缺陷的重建。最后,我们使用一个解码模块,以恢复正常的表面表面背景。此外,一个两阶段培训战略用于精确的精细化精度精度精确的精度改进,在地下深度检查中进行精度检查,我们利用一个通过合作性测试方法,用一个实地测试方法进行实地测试。我们利用一个基础的实地测试。我们用一个基础测试方法,用一个基础化方法,用一个实地测试方法,用一个基础化方法来检验。