Abstract: Monocular vision detection system has simple structure as well as better real time with low cost and convenient and fast detection services, but its detection accuracy is lower than that of the multi-ocular vision system and is highly dependent on the calculation methods. The current vehicle ranging method using monocular vision does not fully consider the error problem caused by multi-scale vehicles, which affects its detection accuracy. A vehicle distance detection method based on multi-task cascaded convolutional neural network (MTCNN) was established, which could calculate space between vehicles through the P4P principle with the vehicle license plate as the target, using the monocular camera to collect front vehicle images and MTCNN algorithm so as to obtain the corner coordinates of the license plate. The proposed method was not related to the size of the vehicle type or road undulation, but only to the quality of license plate image recognition and calculation, which can effectively reduce errors caused by other factors. The test shows that the proposed vehicle distance measurement method uses MTCNN and P4P algorithms to analyze and calculate the video images of the front vehicle, achieving high-precision distance detection. The average error of distance detection within a range of 27 meters is 2.77%, with an average error of 2.52% for detection between 3 and 27 meters. It has high stability in a larger distance measurement range.

Key words: traffic and transportation engineering; monocular vision; vehicle distance measurement; MTCNN; P4P algorcithm; image recognition

摘要： 单目视觉检测系统结构简单、成本低廉、检测方便快捷、实时性好，但检测精度相对多目视觉系统较低，高度依赖计算方法。目前采用单目视觉的车辆测距方法没有充分考虑多尺度车辆导致的误差问题，使其检测精度受到影响。建立一种基于多任务级联卷积神经网络（MTCNN）的车距检测方法，以车辆号牌作为靶标，利用单目摄像头采集前车图像，采用MTCNN算法检测车牌，获取车牌角点坐标，依据P4P原理计算车辆间距。该方法与车型大小、道路起伏无关，仅与车牌图像识别测算质量相关，可以有效减少其它因素导致的误差。试验表明，提出的车辆测距方法通过MTCNN和P4P算法分析计算前车视频图像，实现了较高精度的车距检测，对27 m范围内的车距检测平均误差为2.77%，其中3~27 m的检测平均误差为2.52%，在较大测距范围内具有较高的稳定性。

关键词: 交通运输工程；单目视觉；车辆测距；多任务级联卷积神经网络；P4P算法；图像识别