Enhancing Parking Aid Systems by Leveraging Parking Sign Recognition

With increasing urbanization and the growing number of vehicles in cities, parking challenges are becoming more significant. Traditional parking systems often fail to provide sufficient information about available parking spaces, resulting in drivers wasting valuable time searching for a spot. This results in higher fuel consumption, increased emissions, and additional strain on traffic networks. Some large cities have implemented smart solutions that use sensor systems to display real-time parking availability. However, poor signage and unclear regulations can still make it difficult for drivers to find legal parking. This paper examines the potential for enhancing parking assistance systems by detecting and recognizing parking signs. Parking signs contain essential information about parking rules and space availability, but their diversity and complexity pose challenges for efficient analysis. The primary objective of this study was to conduct a preliminary investigation into the feasibility of developing an automated system for detecting and recognizing parking signs. To support this aim, a dedicated dataset of parking sign images was compiled and used to train a YOLO-based object detection model, enabling accurate localization and classification of parking signs within urban scenes. We then applied text recognition techniques to extract regulatory information from the detected signs and convert it into explicit, machine-readable parking rules. This approach lays the foundation for practical applications in automated parking management systems.