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Smart city article details

Title	Deep Learning Based Efficient Parking Management System Framework
ID_Doc	17836
Authors	Sathishkumar P.; Boopalan R.; Shree S.K.; Dhanish R.
Year	2024
Published	2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024
DOI	http://dx.doi.org/10.1109/ICKECS61492.2024.10616768
Abstract	In urban areas, efficient parking management is crucial for optimizing traffic flow and enhancing overall transportation systems. This paper introduces a novel approach to parking management utilizing the YOLO v5 (You Only Look Once) deep learning architecture. YOLO v5 is renowned for its real-time object detection capabilities, making it an ideal candidate for applications in dynamic environments such as parking lots. By integrating YOLO v5 into our framework, we achieve accurate and fast detection of vacant parking spaces in real-time. This enables proactive decision-making for drivers seeking parking spots, thereby reducing congestion and improving overall traffic efficiency. We present experimental results demonstrating the effectiveness and efficiency of our proposed system compared to traditional methods. Our approach not only enhances parking management efficiency but also lays the foundation for intelligent transportation systems in smart cities. © 2024 IEEE.
Author Keywords	Computer Vision; Deep Learning; Intelligent Transport system; Parking Management; YOLO v5

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