Presented at the International Conference on Advances in Electrical-Electronics Engineering and Computer Science (ICEEECS2024), Ankara, Türkiye, Nov 09, 2024

SETSCI Conference Proceedings, 2024, 19, Page (s): 42-46 , https://doi.org/10.36287/setsci.19.9.042

Published Date: 21 November 2024    | 201     0

The rapid urbanization and exponential growth in vehicle numbers have significantly increased the demand for parking spaces in metropolitan areas, creating challenges for drivers and urban planners alike. Effective detection of available parking spaces is crucial, as it impacts traffic flow, environmental sustainability, public safety, and the efficient use of urban land. Studies show that a significant portion of urban traffic consists of vehicles searching for parking, leading to increased energy consumption, higher emissions, and more congestion. This paper explores the use of advanced parking detection systems, specifically leveraging satellite technology and single-stage object detection algorithm Single Shot Multibox Detector (SSD). By analyzing the performance of the SSD model in detecting empty parking spaces from satellite images, this study offers a comprehensive evaluation of its strengths and weaknesses in various scenarios. The findings contribute to the ongoing development of smart parking solutions, which are essential for reducing environmental impacts, enhancing safety, and improving the quality of life in urban environments. This study is among the first to assess the SSD model's effectiveness in this critical area of urban infrastructure.  

Keywords - parking management, smart city, deep learning, object detection, SSD

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