Abstract
Unsupervised anomaly detection using only normal samples is of great significance for quality inspection in industrial manufacturing. Although existing reconstruction-based methods have achieved promising results, they still face two problems: poor distinguishable information in image reconstruction and well abnormal regeneration caused by model over-generalization ability. To overcome the above issues, we convert the image reconstruction into a combination of parallel feature restorations and propose a multi-feature reconstruction network, MFRNet, using crossed-mask restoration in this paper. Specifically, a multi-scale feature aggregator is first developed to generate more discriminative hierarchical representations of the input images from a pre-trained model. Subsequently, a crossed-mask generator is adopted to randomly cover the extracted feature map, followed by a restoration network based on the transformer structure for high-quality repair of the missing regions. Finally, a hybrid loss is equipped to guide model training and anomaly estimation, which gives consideration to both the pixel and structural similarity. Extensive experiments show that our method is highly competitive with or significantly outperforms other state-of-the-arts on four public available datasets and one self-made dataset.
Abstract (translated)
无监督异常检测仅使用正常样本在工业制造业的质量检测中具有重要意义。尽管现有的基于重构的方法已经取得了很好的效果，但它们仍然面临两个问题：图像重构中的信息模糊和由模型过拟合能力引起的异常再生。为解决这些问题，本文将图像重构转化为跨模态恢复的组合，并提出了一种多特征重构网络，MFRNet，使用双向掩码恢复。具体来说，首先开发了一个多尺度特征聚合器，从预训练模型中生成输入图像的更有区分性的层次表示。然后，采用双向掩码生成器随机覆盖提取的特征图，接着是基于Transformer结构的高质量修补缺失区域的修复网络。最后，配备了一种混合损失函数来指导模型训练和异常估计，考虑了像素结构和相似性。大量实验证明，我们的方法在四个公开可用数据集和自建数据集上与其他最先进的水平具有高度竞争性或显著优于其他方法。
URL
https://arxiv.org/abs/2404.13273
