China Traffic Sign Detection Dataset

With the rapid development of autonomous driving technology, traffic sign detection has become a crucial element to ensure driving safety. However, existing technologies face challenges in recognizing signs in complex traffic environments, especially in situations involving changing lighting, sign occlusion, and diverse sign types. Traditional solutions underperform in handling data diversity and real-world scenarios. This dataset aims to enhance the accuracy and robustness of autonomous driving systems in complex environments by providing high-quality sign detection training data. The dataset is collected using high-resolution cameras under various weather and lighting conditions, including urban and rural roads. Data goes through multiple rounds of annotation and consistency checks to ensure annotation accuracy. The team consists of traffic field experts and image processing professionals, ensuring expertise and annotation quality. Preprocessing steps include image filtering, color correction, and data augmentation. The data is stored in 'JPG' format, categorized by scene, and offers diverse interface options for download and API calls. Strict quality control of the dataset ensures annotation accuracy above 95% and covers 98% of traffic sign types. The innovation lies in applying deep learning-based data augmentation and annotation methods, significantly improving model detection accuracy. In practical applications, this dataset improves sign detection accuracy by about 20% and, compared to similar datasets, covers more sign types and has a higher update frequency, showcasing excellent scalability and versatility.