In surface defect detection, due to the extreme imbalance in the number of positive and negative samples, positive-samples-based anomaly detection methods have received more and more attention. Specifically, reconstruction-based methods are the most popular. However, existing methods are either difficult to repair abnormal foregrounds or reconstruct clear backgrounds. Therefore, we propose a clear memory-augmented auto-encoder (CMA-AE). At first, we propose a novel clear memory-augmented module (CMAM), which combines the encoding and memoryencoding in a way of forgetting and inputting, thereby repairing abnormal foregrounds and preserving clear backgrounds. Secondly, a general artificial anomaly generation algorithm (GAAGA) is proposed to simulate anomalies that are as realistic and feature-rich as possible. At last, we propose a novel multi scale feature residual detection method (MSFR) for defect segmentation, which makes the defect location more accurate. Extensive comparison experiments demonstrate that CMA-AE achieves state-of-the-art detection accuracy and shows great potential in industrial applications.

翻译：在表面缺陷检测中，由于正负样本数量极端不平衡，基于正样本的异常检测方法日益受到关注。其中，基于重建的方法最为流行。然而，现有方法要么难以修复异常前景，要么无法重建清晰背景。为此，我们提出了一种清晰记忆增强自编码器（CMA-AE）。首先，我们提出了一种新型的清晰记忆增强模块（CMAM），该模块以遗忘与输入方式结合编码与记忆编码，从而修复异常前景并保留清晰背景。其次，我们提出了一种通用人工异常生成算法（GAAGA），以模拟尽可能真实且特征丰富的异常。最后，针对缺陷分割，我们提出了一种新颖的多尺度特征残差检测方法（MSFR），使得缺陷定位更加精确。大量对比实验表明，CMA-AE达到了最先进的检测精度，并在工业应用中展现出巨大潜力。