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Developing Industry 4.0 Smart Parking Through Deep Learning and IoT-Based for Electric Vehicle
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Author(s): Marwa Ben Arab (Electrical Systems and Renewable Energies Laboratory, National Engineering School of Sfax, University of Sfax, Tunisia), Mouna Rekik (Electrical Systems and Renewable Energies Laboratory, National Engineering School of Sfax, University of Sfax, Tunisia)and Lotfi Krichen (Electrical Systems and Renewable Energies Laboratory, National Engineering School of Sfax, University of Sfax, Tunisia)
Copyright: 2024
Pages: 18
Source title:
Semantic Web Technologies and Applications in Artificial Intelligence of Things
Source Author(s)/Editor(s): Fernando Ortiz-Rodriguez (Tamaulipas Autonomous University, Mexico), Amed Leyva-Mederos (Universidad Central "Marta Abreu" de Las Villas, Cuba), Sanju Tiwari (Tamaulipas Autonomous University, Mexico), Ania R. Hernandez-Quintana (Universidad de La Habana, Cuba)and Jose L. Martinez-Rodriguez (Autonomous University of Tamaulipas, Mexico)
DOI: 10.4018/979-8-3693-1487-6.ch005
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Abstract
Object detection is central to computer vision, drawing significant attention lately. Deep learning techniques shine for their precision, robustness, and speed. Their integration into Industry 4.0 is widely recognized, especially in AI-powered smart parking systems. This fusion is swiftly advancing, bolstering Industry 4.0 smart parking management and security. This chapter introduces a comprehensive framework presenting both software and hardware components, along with a mixing methodology, to enhance industry smart parking through detecting electric vehicles. The foundation of this approach lies in the application of deep learning, specifically utilizing the YOLOv3 methodology. In addition, the internet of things (IoT) is leveraged, employing a Raspberry Pi4 platform. The methodology for the development and execution of the system is outlined step by step to provide a clear understanding. This integrated solution showcases the detailed practical implementation. As a result, the detection of two vehicles has achieved confidence scores exceeding 0.7.
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