Industrial production often faces a variety of complex working conditions that lead to various defects, including Mura, on the surfaces of various industrial products. We propose a reconstruction network called RecTransformer, which is developed with a transformer for anomaly inpainting. RecTransformer is designed to effectively detect various types of surface defects despite using only a small number of defect samples. RecTransformer simplifies the defect detection problem to a patch-level image completion problem. Without using convolution, the given block image is processed by the transformer model to generate a defect-free reconstructed image. Herein, global semantic information is established, and an attention mechanism is built in the patch sequence, and the spatial information of the patches is determined by position encoding to complete the global image reconstruction process. With a limited number of defect samples as training data, the RecTransformer algorithm accurately reconstructs defects. It achieves an area under the receiver operating characteristic curve score of 97.6% for pixel-level segmentation on the testing dataset. Experiments conducted on a universal surface defect dataset demonstrate the effectiveness of the RecTransformer algorithm. RecTransformer can be adapted to detect various types of surface defects, including Mura in display devices, with only a small number of defect samples.

中文翻译：

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基于变压器的表面缺陷视觉检测算法

工业生产经常面临各种复杂的工况，导致各种工业产品表面出现各种缺陷，包括Mura。我们提出了一个名为 RecTransformer 的重建网络，它是用一个用于异常修复的变压器开发的。RecTransformer 旨在有效检测各种类型的表面缺陷，尽管仅使用少量缺陷样本。RecTransformer 将缺陷检测问题简化为补丁级图像补全问题。在不使用卷积的情况下，给定的块图像由变换器模型处理以生成无缺陷的重建图像。这里，建立了全局语义信息，并在补丁序列中建立了注意力机制，通过位置编码确定块的空间信息，完成全局图像重建过程。RecTransformer算法以有限数量的缺陷样本作为训练数据，准确地重建缺陷。它在测试数据集上实现了像素级分割的 97.6% 的接收者操作特征曲线下面积得分。在通用表面缺陷数据集上进行的实验证明了 RecTransformer 算法的有效性。RecTransformer可以适用于检测各种类型的表面缺陷，包括显示设备中的Mura，且缺陷样本数量很少。它在测试数据集上实现了像素级分割的 97.6% 的接收者操作特征曲线下面积得分。在通用表面缺陷数据集上进行的实验证明了 RecTransformer 算法的有效性。RecTransformer可以适用于检测各种类型的表面缺陷，包括显示设备中的Mura，且缺陷样本数量很少。它在测试数据集上实现了像素级分割的 97.6% 的接收者操作特征曲线下面积得分。在通用表面缺陷数据集上进行的实验证明了 RecTransformer 算法的有效性。RecTransformer可以适用于检测各种类型的表面缺陷，包括显示设备中的Mura，且缺陷样本数量很少。