面向工业异常检测与定位的基于梯度上升的统一异常合成策略
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
AI总结：
针对现有异常合成策略覆盖范围与可控性不足、难处理微弱缺陷的问题，提出GLASS统一框架，结合特征级全局与图像级局部异常合成，用梯度上升引导高斯噪声可控合成近分布异常，在多数据集及工业织物缺陷检测中达SOTA水平。
摘要：
异常合成策略可有效增强无监督异常检测性能。但现有策略在异常合成的覆盖范围与可控性方面存在局限，尤其是针对与正常区域高度相似的微弱缺陷。本文提出全局与局部异常协同合成策略（Global and Local Anomaly co-Synthesis Strategy，GLASS），这是一种全新的统一框架，旨在通过特征层面的全局异常合成（Global Anomaly Synthesis，GAS）的流形与超球分布约束，以及图像层面的局部异常合成（Local Anomaly Synthesis，LAS），合成覆盖范围更广的异常。该方法借助梯度上升引导的高斯噪声与截断投影，以可控方式合成接近分布内的异常。GLASS在MVTec AD数据集上取得了当前最优结果（检测AUROC达99.9%），在VisA和MPDD数据集上同样表现优异，且在微弱缺陷检测方面性能突出。其有效性与效率已在机织物缺陷检测的工业应用中得到进一步验证。代码与数据集可访问：https://github.com/cqylunlun/GLASS。
英文摘要：
Anomaly synthesis strategies can effectively enhance unsupervised anomaly detection. However, existing strategies have limitations in the coverage and controllability of anomaly synthesis, particularly for weak defects that are very similar to normal regions. In this paper, we propose Global and Local Anomaly co-Synthesis Strategy (GLASS), a novel unified framework designed to synthesize a broader coverage of anomalies under the manifold and hypersphere distribution constraints of Global Anomaly Synthesis (GAS) at the feature level and Local Anomaly Synthesis (LAS) at the image level. Our method synthesizes near-in-distribution anomalies in a controllable way using Gaussian noise guided by gradient ascent and truncated projection. GLASS achieves state-of-the-art results on the MVTec AD (detection AUROC of 99.9\%), VisA, and MPDD datasets and excels in weak defect detection. The effectiveness and efficiency have been further validated in industrial applications for woven fabric defect detection. The code and dataset are available at: \url{https://github.com/cqylunlun/GLASS}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
