An OpenCV-Driven Smart Parking Framework

Authors

  • R. Narmatha PG Scholar, Department of BDA, Karpaga Vinayaga College of Engineering and Technology, Chengalpattu, Tamil Nadu, India Author
  • Dr. C. Jayapratha Professor, Department of CSE, Karpaga Vinayaga College of Engineering and Technology, Chengalpattu, Tamil Nadu, India Author

DOI:

https://doi.org/10.32628/IJSRST251259

Keywords:

CNN, YOLO, WSN, RFID

Abstract

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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References

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Published

03-07-2025

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Section

Research Articles