Light-YOLOv3: License Plate Detection in Multi-Vehicle Scenario

DOI Web Site 1 Citations 13 References
  • SUN Yuchao
    College of Internet of Things, Nanjing University of Posts and Telecommunications
  • PENG Qiao
    College of Internet of Things, Nanjing University of Posts and Telecommunications
  • ZHANG Dengyin
    College of Internet of Things, Nanjing University of Posts and Telecommunications

Description

<p>With the development of the Internet of Vehicles, License plate detection technology is widely used, e.g., smart city and edge senor monitor. However, traditional license plate detection methods are based on the license plate edge detection, only suitable for limited situation, such as, wealthy light and favorable camera's angle. Fortunately, deep learning networks represented by YOLOv3 can solve the problem, relying on strict condition. Although YOLOv3 make it better to detect large targets, its low performance in detecting small targets and lack of the real-time interactively. Motivated by this, we present a faster and lightweight YOLOv3 model for multi-vehicle or under-illuminated images scenario. Generally, our model can serves as a guideline for optimizing neural network in multi-vehicle scenario.</p>

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