SOURCE-LINKED INTELLIGENCE
Structured-Prior-Guided Diffusion Inpainting with Physical Consistency for Traffic Sign Augmentation
Traffic sign detection faces a long-tailed data distribution. Many rare signs matter as much as common ones from a regulatory standpoint, yet they have very few samples. Generative data augmentation is one way out. General-purpose inpainting models, however, distort digits, deform geometry and perspective, and shift colours when applied directly to sign regions. We trace this to a single gap: the conditioning signal is too abstract for the physical composition of a sign. We propose a structured-prior-guided diffusion inpainting framework with physical consistency. It injects the semantic, appe
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T09:22:23.000Z
First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.