论文标题
使用卷积神经网络检测多语言数字板
Multilanguage Number Plate Detection using Convolutional Neural Networks
论文作者
论文摘要
对象检测是最近技术的流行研究领域。近年来，深刻的学习表现吸引了研究人员在许多应用中使用它。数十年来分析了数字板（NP）检测和分类，但是它需要更精确，状态，语言和设计独立的方法，因为汽车现在很容易地从状态转移到另一个状态。在本文中，我们提出了一种新的策略，以检测NP并理解NP的国家，语言和布局。提出了具有重新连接属性提取器心脏的YOLOV2传感器用于NP检测，并建议使用全新的卷积神经网络架构进行分类。检测器的平均精度为99.57％，国家，语言和布局分类精度为99.33％。结果表现优于以前的大多数作品，可以将该地区朝向国际NP检测和认可。
Object Detection is a popular field of research for recent technologies. In recent years, profound learning performance attracts the researchers to use it in many applications. Number plate (NP) detection and classification is analyzed over decades however, it needs approaches which are more precise and state, language and design independent since cars are now moving from state to another easily. In this paperwe suggest a new strategy to detect NP and comprehend the nation, language and layout of NPs. YOLOv2 sensor with ResNet attribute extractor heart is proposed for NP detection and a brand new convolutional neural network architecture is suggested to classify NPs. The detector achieves average precision of 99.57% and country, language and layout classification precision of 99.33%. The results outperforms the majority of the previous works and can move the area forward toward international NP detection and recognition.
