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作 者:李响[1,2] 张瑷霖 李国正 赖本涛[1] 陈梦君 Li Xiang;Zhang Ailin;Li Guozheng;Lai Bentao;Chen Mengjun(School of Transportation Engineering,East China Jiaotong University,Nanchang 330013,China;Jiangxi Open University,Nanchang 330046,China;School of Mechanical,Electronic and Control engineering,Beijing Jiaotong University,Beijing 100044,China)
机构地区:[1]华东交通大学交通运输工程学院,南昌330013 [2]江西开放大学,南昌330046 [3]北京交通大学机械与电子控制工程学院,北京100044
出 处:《仪器仪表学报》2025年第2期196-208,共13页Chinese Journal of Scientific Instrument
基 金:国家自然科学基金项目(51965021);江西省教育厅科学技术研究项目(GJJ2403503,GJJ2403501)资助。
摘 要:为了实现城市道路视频监控场景下交通流噪声的快速准确估计,提出一种基于计算机视觉的城市道路噪声实时估计方法。首先,从道路交通噪声产生机理分析入手,提出了一系列基于计算机视觉的城市道路交通噪声相关交通流信息提取方法,改善传统方法提取交通流信息不够便捷的情况。其次,针对传统算法噪声估计准确度不高的问题,进行城市道路交通噪声影响因素分析,将交通流特征与环境特征相结合,构建了基于机器学习的道路交通噪声估计模型,提高了城市道路噪声估计的准确性。最后,分析城市道路交通噪声短时变化规律,确定其尺度可变的特征提取时间窗口,提出了一整套城市道路交通噪声实时估计方案,提升了城市道路噪声估计的实时性。实验结果表明,所提出的基于计算机视觉的交通流信息提取方法较常用的目标检测和目标追踪算法能够更准确的提取城市道路交通噪声相关信息;所建立的城市道路交通噪声估计模型相比传统噪声估计模型有更高的实时性和准确性,相较于现有基于机器学习的噪声估计方法在不同场景下均有着更为准确的估计结果,提高了城市道路噪声估计的准确性和实时性,确定了时间尺度为3和10 min的噪声估计方法,具有实际应用价值。To achieve rapid and accurate estimation of traffic noise in urban road video surveillance scenarios,a real-time noise estimation method based on computer vision is proposed.First,starting with an analysis of the mechanisms behind road traffic noise,a series of computer vision-based methods for extracting traffic flow information related to urban road noise are introduced,improving the convenience of traditional methods for extracting traffic flow data.Secondly,to address the low accuracy of traditional noise estimation algorithms,an analysis of the factors influencing urban road traffic noise is conducted.By combining traffic flow features with environmental factors,a machine learning-based model for traffic noise estimation is developed,enhancing the accuracy of urban road noise estimation.Finally,the short-term variation patterns of urban road traffic noise are analyzed,and a variable-scale feature extraction time window is determined.A complete real-time noise estimation solution is proposed,improving the real-time performance of noise estimation.Experimental results show that the proposed computer vision-based traffic flow information extraction method outperforms commonly used object detection and tracking algorithms in accurately extracting traffic noise-related information.The developed model for traffic noise estimation offers better real-time performance and accuracy compared to traditional models and provides more accurate estimates in various scenarios compared to existing machine learning-based noise estimation methods.The noise estimation methods with time scales of 3 and 10 minutes are validated,demonstrating practical application value.
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