Gate Control System Using Automatic Number Plate Recognition Based on Machine Learning
编号:36 访问权限:仅限参会人 更新:2025-11-19 09:20:25 浏览:1次 拓展类型2

报告开始:暂无开始时间(Asia/Amman)

报告时间:暂无持续时间

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摘要
A gate control system using ANPR (Automatic Number Plate Recognition) is very popular, as many companies offer various gate control options. Typically, the ANPR-based gate control system captures images of license plates with cameras and converts the images into characters using OCR (Optical Character Recognition). Then, the extracted number is checked against a database; if it matches, the gate opens; if not, it stays closed. In this paper, we develop a machine learning-based gate control system using ANPR. First, the system captures images of approaching vehicles with a camera. Next, the YOLOv8 algorithm is used to detect license plates and vehicles. Then, a license plate image is extracted and converted to text with OCR. The vehicle number is compared to the stored number in a database. Finally, the gate opens if the vehicle number matches; otherwise, it remains closed. Our machine learning-based gate control system demonstrates high accuracy and effectiveness in detecting license plates and vehicles. It has been thoroughly tested, with 2,395 detections in total, of which 2,370 were correct and 48 were incorrect, achieving an accuracy of 97.99%.
关键词
gate automation, ANPR, computer vision, machine learning, YOLOv8
报告人
Kazuhiro Muramatsu
Assistant Professor College of Science and Technology, Royal University of Bhutan

稿件作者
Nima Tshering College of Science and Technology, Royal University of Bhutan
Tirthaman Rasaily College of Science and Technology, Royal University of Bhutan
Ugyen Dorji College of Science and Technology, Royal University of Bhutan
Tenzin Dorji College of Science and Technology, Royal University of Bhutan
Pema Zangmo College of Science and Technology, Royal University of Bhutan
Kazuhiro Muramatsu College of Science and Technology, Royal University of Bhutan
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重要日期
  • 会议日期

    12月29日

    2025

    12月31日

    2025

  • 11月30日 2025

    初稿截稿日期

  • 12月30日 2025

    报告提交截止日期

  • 12月30日 2025

    注册截止日期

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