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.

本研究提出了一种清晰记忆增强自编码器来解决表面缺陷检测中正负样本极度不平衡的问题。该自编码器采用清晰记忆增强模块 (CMAM) 来修复异常前景并保留清晰背景，同时结合了通用的人工异常生成算法 (GAAGA) 和多尺度特征残留检测算法 (MSFR) 实现精准的缺陷分割。实验结果表明，CMA-AE 在表面缺陷检测方面的性能很好，有很大的实用潜力。