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Smart Parking Locks Based on Extended UNET-GWO-SVM Algorithm.

Jianguo Shen1, Yu Xia1, Hao Ding1

  • 1College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321000, China.

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Summary
This summary is machine-generated.

This study introduces a smart parking lock system using Raspberry Pi and a novel UNET-GWO-SVM algorithm for accurate license plate recognition. Field tests show a 99.0% unlocking rate, addressing urban parking challenges.

Keywords:
Raspberry PiU-netautomatic license plate recognitiongray wolf optimizationparking lockssupport vector machine

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Area of Science:

  • Engineering
  • Computer Science
  • Urban Planning

Background:

  • Rapid urbanization and increased private car ownership in China exacerbate parking scarcity and conflicts.
  • Existing parking locks lack intelligence and fail to meet diverse user needs.
  • Intelligent parking solutions are crucial for efficient urban mobility.

Purpose of the Study:

  • To develop an intelligent smart parking lock system to mitigate parking conflicts.
  • To enhance license plate recognition accuracy in embedded systems for automated parking access.
  • To provide a user-friendly mobile interface for parking lock control.

Main Methods:

  • Utilized a Raspberry Pi as the core controller for the smart parking lock.
  • Integrated an ultrasonic sensor for vehicle detection and a camera for license plate image capture.
  • Developed a novel UNET-GWO-SVM algorithm for high-accuracy license plate recognition on embedded systems.
  • Created a WeChat mini-program for seamless user interaction and lock control.

Main Results:

  • Achieved an effective unlocking rate of 99.0% with recognition errors under two license plate characters.
  • Maintained an average time consumption of approximately 2 seconds, meeting real-time requirements.
  • Demonstrated the system's feasibility and performance through field tests on a university campus.

Conclusions:

  • The developed smart parking lock system effectively addresses urban parking space conflicts.
  • The UNET-GWO-SVM algorithm significantly improves license plate recognition accuracy in resource-constrained environments.
  • The integrated system offers a practical and efficient solution for intelligent parking management.