Structured-Prior-Guided Diffusion Inpainting with Physical Consistency for Traffic Sign Augmentation
"This study addresses the challenges of long-tail distribution and the scarcity of rare traffic signs by proposing a structured prior-guided diffusion framework for generating physically consistent images. The method injects semantic, appearance, and geometric priors through three orthogonal paths and enforces physical consistency via color and edge structure losses. By leveraging JSON text prompts, front-view vector templates, and affine-aligned vector templates, with Stable Diffusion 1.5 as the backbone, this approach outperforms seven state-of-the-art methods in terms of reconstruction fidelity, physical consistency, and semantic controllability. Notably, it achieves a 91.1% OCR accurate match rate, significantly enhancing the detection performance of rare categories."