论文标题
智能运输系统中改进的Yolov3对象分类
Improved YOLOv3 Object Classification in Intelligent Transportation System
论文作者
论文摘要
近年来，智能运输系统（ITS）中车辆和驾驶员检测的技术是一个热门话题。特别是，驾驶员检测仍然是一个具有挑战性的问题，它是为监督交通秩序和维护公共安全的导电。在本文中，提出了一种基于Yolov3的算法，以实现对高速公路上车辆，驾驶员和人员的检测和分类，以实现区分驾驶员和乘客的目的，并在车辆和驾驶员之间形成一对一的对应关系。提出的模型和对比实验是在我们的自我构建交通驱动程序的面部数据库上进行的。我们提出的算法的有效性通过广泛的实验验证，并在各种复杂的高速公路条件下进行了验证。与其他先进的车辆和驾驶员检测技术相比，该模型的性能良好，并且对路线阻塞，不同的态度和极端照明非常强大。
The technology of vehicle and driver detection in Intelligent Transportation System(ITS) is a hot topic in recent years. In particular, the driver detection is still a challenging problem which is conductive to supervising traffic order and maintaining public safety. In this paper, an algorithm based on YOLOv3 is proposed to realize the detection and classification of vehicles, drivers, and people on the highway, so as to achieve the purpose of distinguishing driver and passenger and form a one-to-one correspondence between vehicles and drivers. The proposed model and contrast experiment are conducted on our self-build traffic driver's face database. The effectiveness of our proposed algorithm is validated by extensive experiments and verified under various complex highway conditions. Compared with other advanced vehicle and driver detection technologies, the model has a good performance and is robust to road blocking, different attitudes, and extreme lighting.
