Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection

• URL: http://arxiv.org/abs/2607.12245v1
• Date: Tue, 14 Jul 2026 01:14:58 GMT
• Title: Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection
• Abstract summary: This paper proposes rough path signature-guided geometry augmentation (RPS-GA)<n>RPS-GA is a geometry-aware approach in which Canny edge contours are treated as ordered planar paths.<n>Results indicate that second-order path-signature geometry offers a practical way to strengthen few-shot industrial defect detection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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