通过结构化噪声与正则化提升工业缺陷检测精度
Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection
- 用结构化扰动模拟缺陷，让自编码器学习修复损伤图像
- 在遮蔽上叠加高斯噪声，使重建函数的雅可比趋近单位阵
- 在MVTec AD数据集上达到99.9/99.4的检测与分割性能
异常检测在自动化工业质检中至关重要，旨在识别原本均匀视觉模式中的细微或罕见缺陷。由于无法收集所有可能异常的代表性样本，本文采用自监督自编码器，使其学习修复受损输入。为此，我们引入一种扰动模型，向训练图像注入人工破坏以模拟结构性缺陷。与去噪自编码器类似，但有两个关键差异：首先，不使用无结构的独立同分布噪声，而是施加空间相干的结构化扰动，使任务兼具分割与补全特性；其次，反直觉地在遮蔽区域叠加并保留高斯噪声，作为蒂柯诺夫正则化项，将重建函数的雅可比锚定至单位阵。这种身份锚定正则化稳定了重建过程，进一步提升了检测与分割准确率。在MVTec AD基准测试中，该方法取得当前最优结果（I/P-AUROC: 99.9/99.4），验证了理论框架的有效性，并展示了其在自动检测中的实际价值。
原文摘要 · Abstract (English)
Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns. As collecting representative examples of all possible anomalies is infeasible, we tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs. To this end, we introduce a corruption model that injects artificial disruptions into training images to mimic structural defects. While reminiscent of denoising autoencoders, our approach differs in two key aspects. First, instead of unstructured i.i.d.\ noise, we apply structured, spatially coherent perturbations that make the task a hybrid of segmentation and inpainting. Second, and counterintuitively, we add and preserve Gaussian noise on top of the occlusions, which acts as a Tikhonov regularizer anchoring the Jacobian of the reconstruction function toward identity. This identity-anchored regularization stabilizes reconstruction and further improves both detection and segmentation accuracy. On the MVTec AD benchmark, our method achieves state-of-the-art results (I/P-AUROC: 99.9/99.4), supporting our theoretical framework and demonstrating its practical relevance for automatic inspection.
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