SignRAG: A Retrieval-Augmented System for Scalable Zero-Shot Road Sign Recognition
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914201645088768 |
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| author | Zhu, Minghao Zhang, Zhihao Sidhu, Anmol Redmill, Keith |
| author_facet | Zhu, Minghao Zhang, Zhihao Sidhu, Anmol Redmill, Keith |
| contents | Automated road sign recognition is a critical task for intelligent transportation systems, but traditional deep learning methods struggle with the sheer number of sign classes and the impracticality of creating exhaustive labeled datasets. This paper introduces a novel zero-shot recognition framework that adapts the Retrieval-Augmented Generation (RAG) paradigm to address this challenge. Our method first uses a Vision Language Model (VLM) to generate a textual description of a sign from an input image. This description is used to retrieve a small set of the most relevant sign candidates from a vector database of reference designs. Subsequently, a Large Language Model (LLM) reasons over the retrieved candidates to make a final, fine-grained recognition. We validate this approach on a comprehensive set of 303 regulatory signs from the Ohio MUTCD. Experimental results demonstrate the framework's effectiveness, achieving 95.58% accuracy on ideal reference images and 82.45% on challenging real-world road data. This work demonstrates the viability of RAG-based architectures for creating scalable and accurate systems for road sign recognition without task-specific training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12885 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SignRAG: A Retrieval-Augmented System for Scalable Zero-Shot Road Sign Recognition Zhu, Minghao Zhang, Zhihao Sidhu, Anmol Redmill, Keith Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Information Retrieval Robotics Automated road sign recognition is a critical task for intelligent transportation systems, but traditional deep learning methods struggle with the sheer number of sign classes and the impracticality of creating exhaustive labeled datasets. This paper introduces a novel zero-shot recognition framework that adapts the Retrieval-Augmented Generation (RAG) paradigm to address this challenge. Our method first uses a Vision Language Model (VLM) to generate a textual description of a sign from an input image. This description is used to retrieve a small set of the most relevant sign candidates from a vector database of reference designs. Subsequently, a Large Language Model (LLM) reasons over the retrieved candidates to make a final, fine-grained recognition. We validate this approach on a comprehensive set of 303 regulatory signs from the Ohio MUTCD. Experimental results demonstrate the framework's effectiveness, achieving 95.58% accuracy on ideal reference images and 82.45% on challenging real-world road data. This work demonstrates the viability of RAG-based architectures for creating scalable and accurate systems for road sign recognition without task-specific training. |
| title | SignRAG: A Retrieval-Augmented System for Scalable Zero-Shot Road Sign Recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Information Retrieval Robotics |
| url | https://arxiv.org/abs/2512.12885 |