Ambient Temperature Estimation of Mountain Freeways Based on Roadside Camera Images

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2024-10-08 DOI:10.1109/JSEN.2024.3472076

Zhu Sun;Yin-Li Jin;Yu-Jie Zhang;Wen-Peng Xu;Li Li

引用次数: 0

Abstract

Accurate estimation of the ambient temperature of mountain freeways enables freeway management agencies to provide weather-related information to drivers. This article proposed an image-based data-driven method, namely the visual temperature estimation network (VTENet), to estimate freeway ambient temperature based on images captured by roadside cameras. The VTENet had a convolutional neural network (CNN) architecture to extract temperature-related image features, and two extra networks to capture space-time information on data collection and time-series image features. The VTENet was trained and tested based on a self-established dataset collected at a mountain freeway. The results showed that the VTENet can estimate freeway ambient temperature with high accuracy. The model gives a more accurate temperature estimation with data collected from 10 to 11 A.M. and 2 to 3 P.M. than other periods. It also performed better using four-day or five-day sequence images than other data inputs.

查看原文本刊更多论文

基于路边摄像头图像的山区高速公路环境温度估算

准确估算山区高速公路的环境温度有助于高速公路管理机构向驾驶员提供与天气相关的信息。本文提出了一种基于图像的数据驱动方法，即视觉温度估算网络（VTENet），根据路边摄像头拍摄的图像估算高速公路的环境温度。VTENet 有一个卷积神经网络（CNN）架构，用于提取与温度相关的图像特征，还有两个额外的网络用于捕捉数据采集的时空信息和时间序列图像特征。VTENet 根据在山区高速公路上收集的自建数据集进行了训练和测试。结果表明，VTENet 能够高精度地估计高速公路的环境温度。与其他时段相比，该模型对上午 10 点至 11 点和下午 2 点至 3 点收集的数据进行的温度估算更为准确。使用四天或五天的序列图像时，其性能也优于其他数据输入。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice