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

DOI Web Site 被引用文献1件 参考文献13件
  • 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

説明

<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>

収録刊行物

被引用文献 (1)*注記

もっと見る

参考文献 (13)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ