Spatial Guidance Sign Image Dataset

Currently, many navigation and autonomous driving systems rely on precise spatial guidance signs for real-time positioning. However, due to the complex and changing urban environment, these systems often face challenges such as low recognition accuracy and slow response times. Existing solutions usually rely on limited datasets for training, making it difficult to cover enough scene variations. This dataset aims to address these issues by providing a large number of image materials from different environments and at different times, enhancing the robustness of recognition models. We use high-performance cameras to collect sign images in various environments such as urban roads, rural paths, and building interiors, supplemented by multi-climate and multi-time collection strategies. Data quality is ensured through multiple rounds of annotation and expert review, with the annotation team composed of computer vision and traffic management experts, totaling more than 50 people. Data preprocessing includes steps such as image denoising, contrast adjustment, and size normalization. The images are stored in JPG format and organized in a classified directory form for quick retrieval and use.