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.

中文翻译：

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基于异常特征编辑的对抗网络的纹理缺陷视觉检测

在工业自动化领域中，建立统一的模型以检查不同纹理表面的缺陷仍然是一个挑战，因为这些表面可能以规则和不规则的方式变化。当前的无监督学习方法仅在无缺陷的样本上进行训练，并且无法在测试过程中直接解决异常问题，这使得这些方法无法同时检查各种纹理缺陷。在本文中，我们提出了一种新颖的基于无监督异常特征编辑的对抗网络（AFEAN），以准确检查各种纹理缺陷。为了使AFEAN具有解决异常的能力，将包含无缺陷图像和人为缺陷图像的配对输入用于训练。首先，AFEAN使用特征提取模块（FEM）提取配对输入的潜在特征。随后，提出了一种新颖的异常特征检测模块（AFDM），以检测潜在空间中人为缺陷图像的异常特征。在提出的AFDM中，提出了一种新的基于中心约束的聚类方法，通过学习潜在特征的分布来检测异常特征。接下来，提出了一种新颖的全局上下文特征编辑模块（GCFEM），用于将检测到的异常特征转换为正常特征以抑制缺陷的重建。最后，特征解码模块（FDM）利用已编辑的特征来重建纹理背景。通过AFDM和GCFEM，AFEAN能够处理异常特征，从而有效地抑制了纹理背景上缺陷的重建。另外，为了进一步提高纹理重建的准确性，像素级判别模块（PDM）用于重建纹理细节。在测试阶段，缺陷会通过残差图像在输入图像和重建的纹理背景之间进行分割。广泛的实验结果表明，AFEAN可以达到最先进的检查精度。