Abstract
Anomaly detection is a crucial task in computer vision, yet collecting real-world defect images is inherently difficult due to the rarity and unpredictability of anomalies. Consequently, researchers have turned to synthetic methods for training data augmentation. However, existing synthetic strategies (e.g., naive cut-and-paste or inpainting) overlook the underlying physical causes of defects, leading to inconsistent, low-fidelity anomalies that hamper model generalization to real-world complexities. In this thesis, we introduced a novel pipeline that generates synthetic anomalies through Math-Physics model guidance, refines them via a Coarse-to-Fine approach and employs a bi-level optimization strategy with a Synthesis Quality Estimator(SQE). By incorporating physical modeling of cracks, corrosion, and deformation, our method produces realistic defect masks, which are subsequently enhanced in two phases. The first stage (npcF) enforces a PDE-based consistency to achieve a globally coherent anomaly structure, while the second stage (npcF++) further improves local fidelity using wavelet transforms and boundary synergy blocks. Additionally, we leverage SQE-driven weighting, ensuring that high-quality synthetic samples receive greater emphasis during training. To validate our approach, we conducted comprehensive experiments on three widely adopted industrial anomaly detection benchmarks: MVTec AD, VisA, and BTAD. Across these datasets, the proposed pipeline achieves state-of-the-art (SOTA) results in both image-AUROC and pixel-AUROC, confirming the effectiveness of our MaPhC2F and BiSQAD.
Abstract (translated)
异常检测是计算机视觉中的一个重要任务，但由于缺陷图像在现实世界中极为稀有且难以预测，收集这些图像非常困难。因此，研究人员转向合成方法来增强训练数据。然而，现有的合成策略（如简单的剪切粘贴或修复）忽略了缺陷的物理成因，导致生成的异常不一致且质量低劣，限制了模型对现实复杂情况的泛化能力。在本论文中，我们引入了一种新型管道，该管道通过数学-物理模型指导生成合成异常，并采用粗到细的方法进行优化，同时利用双层级优化策略和合成质量评估器（SQE）。我们的方法通过对裂缝、腐蚀和变形等缺陷进行物理建模来产生现实的缺陷掩膜，在两个阶段进一步增强它们。第一阶段(npcF)通过基于PDE的一致性实现全局连贯的异常结构，而第二阶段(npcF++)则利用小波变换和边界协同块进一步提升局部细节的真实性。 此外，我们还采用了SQE驱动的加权机制，确保在训练过程中对高质量合成样本给予更多关注。为了验证我们的方法的有效性，在三个广泛采用的工业异常检测基准数据集（MVTec AD、VisA 和 BTAD）上进行了全面实验。在这些数据集中，所提出的管道在图像AUROC和像素AUROC方面均达到了最先进的（SOTA）结果，这证实了我们MaPhC2F和BiSQAD的有效性。
URL
https://arxiv.org/abs/2504.12970
