基于双目立体视觉的目标识别与测距方法

Guan Shuai, Ma Wenlun, Fan Jingjing, Liu Zhipeng
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引用次数: 2

摘要

针对传统无人车环境感知方法成本高、安装受限等问题,本文提出了一种基于YOLOv4与双目立体视觉融合的人员识别与距离测量方法。通过对数据集的标注,利用Darknet深度学习框架对人员进行训练和识别,利用双目摄像机视差数据进行人员距离检测。实验结果表明,该方法的识别精度为0.941,距离误差小于5%,能够满足无人驾驶车辆的任务要求,为解决自动驾驶车辆的环境感知问题提供技术支持。
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Target Recognition and Range-measuring Method based on Binocular Stereo Vision
Aiming at the problems of high cost and limited installation of traditional unmanned vehicle environment perception methods, this paper proposes a method of personnel identification and distance measurement based on the fusion of YOLOv4 and binocular stereo vision. Through the annotation of the data set, the Darknet deep learning framework is used to train and recognize the personnel, and the binocular camera disparity data is used for personnel distance detection. The experimental results show that the recognition accuracy of this method is 0.941 and the distance error is less than 5%, which can meet the task requirements of unmanned vehicle and provide technical support for solving the environment perception problems of autonomous driving vehicle.
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