Intelligent vehicle detection and counting are becoming increasingly important in the field of highway management. However, due to the different sizes of vehicles, their detection remains a challenge that directly affects the accuracy of vehicle counts. To address this issue, this paper proposes a vision-based vehicle detection and counting system. A new high definition highway vehicle dataset with a total of 57,290 annotated instances in 11,129 images is published in this study. Compared with the existing public datasets, the proposed dataset contains annotated tiny objects in the image, which provides the complete data foundation for vehicle detection based on deep learning. In the proposed vehicle detection and counting system, the highway road surface in the image is first extracted and divided into a remote area and a proximal area by a newly proposed segmentation method; the method is crucial for improving vehicle detection. Then, the above two areas are placed into the YOLOv3 network to detect the type and location of the vehicle. Finally, the vehicle trajectories are obtained by the ORB algorithm, which can be used to judge the driving direction of the vehicle and obtain the number of different vehicles. Several highway surveillance videos based on different scenes are used to verify the proposed methods. The experimental results verify that using the proposed segmentation method can provide higher detection accuracy, especially for the detection of small vehicle objects. Moreover, the novel strategy described in this article performs notably well in judging driving direction and counting vehicles. This paper has general practical significance for the management and control of highway scenes.

中文翻译：

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在高速公路场景中使用深度学习的基于视觉的车辆检测和计数系统

智能车辆检测和计数在高速公路管理领域变得越来越重要。但是，由于车辆的大小不同，其检测仍然是一个挑战，直接影响车辆计数的准确性。为了解决这个问题，本文提出了一种基于视觉的车辆检测和计数系统。这项研究发布了一个新的高清公路车辆数据集，在11,129张图像中共有57,290个带注释的实例。与现有的公共数据集相比，提出的数据集在图像中包含带注释的微小对象，这为基于深度学习的车辆检测提供了完整的数据基础。在建议的车辆检测和计数系统中，首先通过新提出的分割方法提取图像中的高速公路路面并将其划分为偏远地区和近端地区。该方法对于改善车辆检测至关重要。然后，将以上两个区域放入YOLOv3网络中以检测车辆的类型和位置。最后，通过ORB算法获得车辆轨迹，该算法可用于判断车辆的行驶方向并获得不同车辆的数量。基于不同场景的几个高速公路监控录像被用来验证所提出的方法。实验结果证明，使用本文提出的分割方法可以提供更高的检测精度，特别是对于小型车辆物体的检测。此外，本文所述的新颖策略在判断行驶方向和计算车辆数量方面表现出色。本文对于高速公路现场的管理与控制具有普遍的现实意义。