提出自同构学习方法，提升工业缺陷检测在非对齐图像中的表现
HomographyAD: Deep Anomaly Detection Using Self Homography Learning
- 通过深度同构估计实现输入图像前景对齐
- 利用自同构学习从正常样本中提取额外形状信息
- 适用于真实工业场景，尤其对非对齐数据效果显著
异常检测（AD）旨在区分正常与异常数据，对制造设施自动化应用至关重要。针对代表性工业环境数据集MVTec，尽管现有方法表现优异，但其性能仅限于完全对齐的数据，难以适应真实工业场景。为此，我们提出HomographyAD，一种基于ImageNet预训练网络的新型深度异常检测方法，专为实际工业数据设计。首先，采用深度同构估计实现输入前景对齐；其次，通过自同构学习微调模型，从正常样本中学习额外形状信息；最后，基于测试样本特征与正常特征分布的距离进行异常检测。在多种现有AD方法上应用该方法，实验表明性能显著提升。
原文摘要 · Abstract (English)
Anomaly detection (AD) is a task that distinguishes normal and abnormal data, which is important for applying automation technologies of the manufacturing facilities. For MVTec dataset that is a representative AD dataset for industrial environment, many recent works have shown remarkable performances. However, the existing anomaly detection works have a limitation of showing good performance for fully-aligned datasets only, unlike real-world industrial environments. To solve this limitation, we propose HomographyAD, a novel deep anomaly detection methodology based on the ImageNet-pretrained network, which is specially designed for actual industrial dataset. Specifically, we first suggest input foreground alignment using the deep homography estimation method. In addition, we fine-tune the model by self homography learning to learn additional shape information from normal samples. Finally, we conduct anomaly detection based on the measure of how far the feature of test sample is from the distribution of the extracted normal features. By applying our proposed method to various existing AD approaches, we show performance enhancement through extensive experiments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。