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使用单目相机的车速测量方法。

Vehicle speed measurement method using monocular cameras.

作者信息

Lian Hao, Li Meian, Li Ting, Zhang Yongan, Shi Yanyu, Fan Yikun, Yang Wenqian, Jiang Huilin, Zhou Peng, Wu Haibo

机构信息

Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.

Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, China.

出版信息

Sci Rep. 2025 Jan 22;15(1):2755. doi: 10.1038/s41598-025-87077-6.

DOI:10.1038/s41598-025-87077-6
PMID:39843469
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11754901/
Abstract

This paper proposes a method for fast and accurate vehicle speed measurement based on a monocular camera. Firstly, by establishing a new camera imaging model, the calibration method for variable focal lengths is optimized, simplifying the transformation process between the four coordinate systems in traditional camera imaging models, and the method does not need to restore the pixel coordinates to dedistortion. Secondly, based on the camera imaging model, a two-dimensional positioning algorithm is proposed. By leveraging the characteristics of the speed measurement problem, the complex three-dimensional positioning problem is simplified into a two-dimensional model, reducing the overall computational complexity of the positioning problem. Finally, the algorithm is combined with You Only Look Once version 7 (YOLOv7) and Deep Simple Online and Realtime Tracking (DeepSORT) algorithms, integrating multiple model structures to optimize the network, achieving precise multi-target speed measurement. Experiments show that under frame-by-frame measurement conditions, the minimum and average accuracies of this method reach 95.1% and 97.6%, respectively. Compared with other methods, it has significant advantages in speed measurement accuracy and computational efficiency. Therefore, this research outcome is expected to play an important role in intelligent transportation systems and road safety management.

摘要

本文提出了一种基于单目相机的快速准确车速测量方法。首先，通过建立新的相机成像模型，优化了可变焦距的校准方法，简化了传统相机成像模型中四个坐标系之间的转换过程，且该方法无需将像素坐标恢复去畸变。其次，基于相机成像模型，提出了一种二维定位算法。利用车速测量问题的特点，将复杂的三维定位问题简化为二维模型，降低了定位问题的整体计算复杂度。最后，将该算法与你只看一次版本7（YOLOv7）和深度简单在线实时跟踪（DeepSORT）算法相结合，整合多个模型结构来优化网络，实现精确的多目标车速测量。实验表明，在逐帧测量条件下，该方法的最小精度和平均精度分别达到95.1%和97.6%。与其他方法相比，它在车速测量精度和计算效率方面具有显著优势。因此，该研究成果有望在智能交通系统和道路安全管理中发挥重要作用。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6d6/11754901/b54b086e8479/41598_2025_87077_Fig11_HTML.jpg
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