Scholarlink

With the growing volume of vehicles on roads, there is a rising need for intelligent systems that can help in real-time traffic monitoring and enforcement. This paper introduces a practical and efficient solution that combines several techniques—vehicle detection, license plate recognition, and colour identification—under one, unified framework. We employ YOLOv8 for precise object detection, Haar Cascade for license plate identification, and Easy OCR to extract registration numbers from them. For vehicle colour identification, the HSV colour space is employed for enhanced classification under varying lighting. The system records video input via a Tapo C310 camera and processes the frames in real time at 30 frames per second. Each module is standalone, meaning that the system can be easily scaled and is always stable even when it is performing intensive processing. We applied our system on a dataset of 500 images and established that it recorded 98.5% accuracy in tracking, 96.8% in plate number recognition, and 95.6% in colour recognition. These achievements indicate that our approach has higher accuracy compared to most common methods. It has real-world applications in law enforcement, toll collection, and intelligent city traffic systems. In the future, we intend to enhance its performance in difficult situations like low illumination and partial occlusion.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

“Amalgam Based Intelligent Road Surveillance Using Machine Learning Algorithm.”

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Deepak Mane,
  • Saloni Khandelwal,
  • Prem Borse,
  • Sahil Sinnarkar,
  • Suhasini Choudhari

摘要

With the growing volume of vehicles on roads, there is a rising need for intelligent systems that can help in real-time traffic monitoring and enforcement. This paper introduces a practical and efficient solution that combines several techniques—vehicle detection, license plate recognition, and colour identification—under one, unified framework. We employ YOLOv8 for precise object detection, Haar Cascade for license plate identification, and Easy OCR to extract registration numbers from them. For vehicle colour identification, the HSV colour space is employed for enhanced classification under varying lighting. The system records video input via a Tapo C310 camera and processes the frames in real time at 30 frames per second. Each module is standalone, meaning that the system can be easily scaled and is always stable even when it is performing intensive processing. We applied our system on a dataset of 500 images and established that it recorded 98.5% accuracy in tracking, 96.8% in plate number recognition, and 95.6% in colour recognition. These achievements indicate that our approach has higher accuracy compared to most common methods. It has real-world applications in law enforcement, toll collection, and intelligent city traffic systems. In the future, we intend to enhance its performance in difficult situations like low illumination and partial occlusion.