An OpenCV-Driven Smart Parking Framework
DOI:
https://doi.org/10.32628/IJSRST251259Keywords:
CNN, YOLO, WSN, RFIDAbstract
The rapid rise in urban vehicle ownership has created major parking challenges, including congestion, time loss, and pollution. Traditional systems often rely on costly, non-scalable sensors and lack real-time monitoring. This paper presents a Smart Parking System (SPS) that uses computer vision, machine learning, and IoT to provide a scalable, accurate, and cost-effective solution. Leveraging cameras and a Convolutional Neural Network (CNN), the system detects parking occupancy with over 95% accuracy and guides drivers to the nearest available spot using an optimized distance algorithm. The architecture includes image capture, real-time processing via OpenCV, cloud storage, a mobile app, and an admin dashboard. Results show improved efficiency, reduced search time, and lower emissions, making SPS a strong candidate for smart city applications. Future enhancements include demand prediction and support for multi-level parking structures.
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