对于非均匀、非周期性欠规则纹理表面缺陷的检测，由于背景纹理为非平稳信 号，常规的图像重建较为困难。同时，由于难以事先获取缺陷的形状、灰度等图像特征 信息，且同类缺陷的视觉特征可呈现较大的分散性，现有的依赖于目标特征的检测方法 难以适用。为此，文中提出了一种基于图像修复的无监督学习表面缺陷检测方法。该方 法需要先将少量正常纹理样本图像作为训练集，用于训练网络模型; 然后在检测时，人 为地在样本图像中设置缺失区域，并利用网络模型预测缺失区域的内容; 最后根据重建 图像与待测图像的结构相似性评价及残差实现缺陷检测与分离。实验结果表明，文中方 法不仅能够有效检测规则纹理表面的缺陷，而且能够检测欠规则纹理表面的缺陷，具有 较强的实用性和适应性。

For the detection of non-uniform and non-periodic irregular texture surface defects，it is difficult to achieve conventional image reconstruction as the background texture is a non-stationary signal． It is also difficult to obtain sufficient information about the defects such as shape，gray scale and other image features in advance． And the visual features of similar defects can present great dispersion． The existing detection methods based on target features are inapplicable． Therefore，this paper proposed an unsupervised learning surface defect detection method based on image inpainting． Firstly，the method used a small number of normal texture samples as training set to train the network model． Then，the missing area was set artificially in the sample image and the network model was used to predict the content of the missing areas during the testing stage． By combining the structural similarity evaluation and residual error of the reconstructed image and the image to be tested，the defect detection and separation were realized． The experimental results show that the proposed method can not only effectively detect the defects on the regular texture surface but also detect the defects of irregular texture surfaces effectively，showing good practicability and adaptability．