用于少样本工业表面缺陷检测的粗糙路径签名引导几何增强
Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection
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中文总结 AI 辅助
针对少样本工业表面缺陷检测难题，提出粗糙路径签名引导几何增强方法，通过处理边缘轮廓二阶签名响应突出边界结构，经实验验证该方法能有效提升少样本检测性能，无需元学习或重新设计探测器。
中文摘要 AI 辅助
少样本工业缺陷检测对标准监督探测器来说仍然困难，在边界主导的工业缺陷上性能不佳。本文提出粗糙路径签名引导几何增强（RPS - GA），将Canny边缘轮廓视为有序平面路径，其截断的二阶签名响应通过SIG - AUG和SGAA两个融合算子聚合成突出边界相关结构的空间图。在NEU - DET和PCB - Defect数据集上评估，结果表明二阶路径签名几何为少样本工业缺陷检测提供了无需元学习或重新设计探测器的实用方法。
英文摘要
Few-shot industrial defect detection remains difficult for standard supervised detectors, which achieve poor performance on boundary-dominated industrial defects. This paper proposes rough path signature-guided geometry augmentation (RPS-GA), a geometry-aware approach in which Canny edge contours are treated as ordered planar paths whose truncated second-order signature responses, especially the antisymmetric Lévy-area term, are aggregated into a spatial map that highlights boundary-related structure through two fusion operators, SIG-AUG and SGAA. The approach is evaluated on NEU-DET and PCB-Defect under a few-shot protocol with 5, 10, 20, or 50 labeled images per class, using an unmodified YOLOv8n detector throughout. Compared with the baseline, RPS-GA delivers large gains when supervision is limited, although the margin shrinks as more labels become available. On NEU-DET, SIG-AUG raises 10-shot mAP@0.5 from 0.341 to 0.583, whereas on PCB-Defect, SGAA improves 10-shot mAP@0.5 from 0.086 to 0.299 and yields usable detection at 5-shot where the baseline fails entirely. These trends are confirmed by multi-seed evaluation across independent random partitions. Overall, the results indicate that second-order path-signature geometry offers a practical way to strengthen few-shot industrial defect detection without meta-learning or detector redesign.
