With the rapid expansion of smart cities and the growing need for accurate and real-time analysis of road infrastructure, the development of artificial intelligence systems capable of perceiving, analyzing, and recording environmental information has become increasingly vital. In this context, the present study introduces a novel system for the detection of Iranian traffic signs, making a significant contribution not only in the data collection domain but also in the detection of visual data. A comprehensive and unique dataset is constructed, consisting of 14,111 images and more than 19,000 traffic signs across 118 distinct classes, has been collected over a two-year period under diverse temporal conditions (morning, noon, dusk, and night) and throughout all four seasons, covering urban, rural, and intercity areas across the country. The image annotation process was conducted with high precision using the MakeSense tool in two standard formats: YOLO (.txt) and Pascal VOC (.xml). Subsequently, an automated detection system was developed based on the advanced deep neural network model YOLOv12, enabling precise identification of traffic signs. The model’s performance, evaluated through 6-Fold Cross Validation, demonstrated outstanding accuracy achieving an mAP@50 of 96%. These results highlight the model’s remarkable efficiency in real-world scenarios and its superiority over previous YOLO versions. Beyond detection capabilities, the proposed system can be employed for the extraction of digital traffic sign maps, serving as a foundational tool for navigation systems, intelligent vehicles, spatial analytics, and the development of a national traffic sign map of Iran and other similar countries. By integrating cutting-edge technologies, data-driven localization, and state-of-the-art deep learning architectures, this research represents a significant step toward a smarter and safer future for the nation’s road networks.
Zarei et al. (Wed,) studied this question.