发表机构
University of Shanghai for Science and Technology（上海理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出GaugeDefect，利用特征传输曲率定位表面细微异常，如划痕和凹痕，通过和乐矩阵偏差检测特征场不一致，适用于曲面和纹理材料。
AI 中文摘要
工业异常定位在基于特征、基于重建和基于蒸馏的方法下取得了快速进展。这些方法大多通过评估一个区域相对于正常训练图像，其局部外观或特征表示有多不寻常来对其进行评分。这是一个强大且实用的公式。在这项工作中，我们研究了一种互补的几何线索，用于异常区域可能仍包含局部合理视觉特征的情况。细小的划痕、小的凹痕和被打乱的重复图案通常不会使每个局部补丁单独异常；相反，它们扰乱了相邻特征在表面上的变化和连接方式。我们提出了GaugeDefect，一种基于特征传输曲率的表面异常定位几何方法。给定一个特征格点，我们在每个节点估计一个局部特征框架，并计算相邻框架之间的正交传输。围绕一个小闭合回路的累积传输给出了一个和乐矩阵，其与单位矩阵的偏差度量了特征传输曲率。在正常训练图像上校准后，异常大的曲率指示特征场中的局部不一致性。这里的曲率不是被检查物体的物理曲率，而是表示空间中邻域不一致性的度量。这使得该方法适用于曲面、纹理材料和非平面工业物体。其主要作用是改进对细微表面扰动的定位，同时作为曲率信号的自然结果，通常在缺陷边界附近产生更尖锐的响应。
英文摘要
Industrial anomaly localization has advanced rapidly with feature-based, reconstruction-based, and distillation-based methods. Most of these methods score a region by asking how unusual its local appearance or feature representation is with respect to normal training images. This is a strong and practical formulation. In this work, we study a complementary geometric cue for cases where an abnormal region may still contain locally plausible visual features. Thin scratches, small dents, and disrupted repeated patterns often do not make every local patch individually abnormal; instead, they disturb how nearby features vary and connect across the surface. We propose GaugeDefect, a geometric method for surface anomaly localization based on the curvature of feature transport. Given a feature lattice, we estimate a local feature frame at each node and compute orthogonal transports between neighboring frames. The accumulated transport around a small closed loop gives a holonomy matrix, whose deviation from identity measures feature-transport curvature. After calibration on normal training images, unusually large curvature indicates a local inconsistency in the feature field. The curvature here is not the physical curvature of the inspected object, but a representation-space measure of neighborhood inconsistency. This makes the method applicable to curved surfaces, textured materials, and non-planar industrial objects. Its main role is to improve localization of subtle surface disruptions, while often producing sharper responses near defect boundaries as a natural consequence of the curvature signal.
CommentsAccepted at the 9th Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2026)
