| 基于机器视觉的车辆检测与参数识别研究进展 |
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| 引用本文： | 孔烜,张杰,邓露,刘英凯. 基于机器视觉的车辆检测与参数识别研究进展[J]. 中国公路学报, 2021, 34(4): 13-30. DOI: 10.19721/j.cnki.1001-7372.2021.04.002 |
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| 作者姓名： | 孔烜 张杰 邓露 刘英凯 |
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| 作者单位： | 1. 湖南大学工程结构损伤诊断湖南省重点实验室, 湖南长沙 410082;2. 湖南大学土木工程学院, 湖南长沙 410082 |
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| 基金项目： | 国家自然科学基金项目（52008160，51778222）；湖南省重点研发计划项目（2017SK2224）；湖南省研究生科研创新项目（CX2018B159） |
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| 摘 要： | 近年来公路交通运输快速增长,交通车辆的快速准确检测与识别对智能交通系统和交通基础设施运维具有重要意义.随着机器视觉和深度学习技术的迅速发展及其在目标检测领域的广泛应用,车辆目标检测和参数识别也取得新的突破.该文从车辆参数的识别方法和应用研究两方面梳理了机器视觉和深度学习在车辆检测与参数识别领域的研究现状、最新研究成果和...
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| 关 键 词： | 交通工程 车辆检测 综述 车辆参数识别 机器视觉 深度学习 卷积神经网络 |
| 收稿时间： | 2020-06-04 |
Research Advances on Vehicle Parameter Identification Based on Machine Vision |
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| KONG Xuan,ZHANG Jie,DENG Lu,LIU Ying-kai. Research Advances on Vehicle Parameter Identification Based on Machine Vision[J]. China Journal of Highway and Transport, 2021, 34(4): 13-30. DOI: 10.19721/j.cnki.1001-7372.2021.04.002 |
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| Authors: | KONG Xuan ZHANG Jie DENG Lu LIU Ying-kai |
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| Affiliation: | 1. Hunan Provincial Key Laboratory for Damage Diagnosis of Engineering Structures, Hunan University, Changsha 410082, Hunan, China;2. School of Civil Engineering, Hunan University, Changsha 410082, Hunan, China |
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| Abstract: | With the rapid growth of highway transportation in recent years, the accurate identification of vehicle parameters is of great significance to the development of intelligent transportation and the operation and maintenance of transportation infrastructure. The rapid development and popularity of machine vision and deep learning in moving object detection have achieved new advances in vehicle parameter identification. The present study aimed to review the current status, new advances, and future trends in vehicle parameter identification based on machine vision and deep learning technologies from the two aspects of detection algorithm and applied research. Regarding detection methods, this paper introduced the basic principles, pros, and cons of these methods by classifying them into three categories:moving object detection, instance object detection, and fine-grained object detection. Research on the identification of vehicle parameters based on machine vision and deep learning was reviewed in detail, including the vehicle type, vehicle spatio-temporal parameters, vehicle weight parameters, and multiple parameter identification systems. Finally, this paper summarized research on vehicle parameters based on machine vision and deep learning, discussing the current challenges and possible future trends. The research demonstrates that suitable vehicle detection methods should be selected according to the actual requirements and the characteristics of each algorithm for different environmental conditions and vehicle parameters. At present, the detection method is still limited to single-vehicle parameters or the independent detection of several parameters. As identification accuracy and efficiency are difficult to meet simultaneously, future research should focus on the integration of new technologies to improve the accuracy, efficiency, robustness, and comprehensive vehicle parameter identification in complex environments for better application in practice. |
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| Keywords: | traffic engineering vehicle object detection review vehicle parameter identification machine vision deep learning convolutional neural network |
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