Edge‐Friendly NAS Framework for Mural Pattern Recognition via Structure‐Aware Feature Fusion

Ancient mural recognition faces unique challenges due to degradation, stylistic variations, and domain‐specific symbolism. We propose a lightweight, edge‐deployable neural architecture search (NAS) framework—SG‐NAS‐MPR—designed for accurate mural pattern recognition. Our framework integrates gated convolutions with frequency‐domain fusion in a structure‐aware module to enhance features under visual noise. A contrast‐aware NAS strategy tailors compact backbones for real‐time inference. Experiments on Dunhuang mural datasets show that our method surpasses existing CNN and NAS models in accuracy (93.4%) and F1‐score (0.922), whereas reducing latency and model size. This work enables efficient and interpretable recognition in cultural heritage computing, supporting mobile museum applications and AR‐based mural analysis.