A new visibility measurement system based on a black target and a comparative trial with visibility instruments

Tang, Fanjie; Ma, Shuqing; Yang, Ling; Du, Chuanyao; Tang, Yingjie

doi:10.1016/j.atmosenv.2016.08.041

Cited by 19 publications

(9 citation statements)

References 8 publications

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“…As a result, many studies introduce artificial blackbodies in the system to establish an absolute brightness reference point in an image. With an artificial blackbody, the performance of the camera-based visiometer is significantly better than those with targets with a higher reflectivity [26], and the observations are consistent with the commercial visibility instruments used under low visibility conditions [27]. To eliminate the influence of the dark current and CCD gain of the video camera, double blackbodies have been adapted [4][5][6].…”

Section: Introductionmentioning

confidence: 86%

Error Analysis and Visibility Classification of Camera-Based Visiometer Using SVM under Nonstandard Conditions

Chen

Wang

et al. 2023

Atmosphere

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A camera-based visiometer is a promising atmospheric visibility measurement tool because it can meet some specific demands such as the need for visibility monitoring in a strong way, whereas traditional instruments, such as forward scatter-type sensors and transmissometers, can hardly be widely utilized due to their high cost. The camera-based method is used to measure visibility by recording the luminance contrast of the objects in an image. However, lacking standard conditions, they can hardly obtain absolute measurements even with blackbody objects. In this paper, the errors caused by nonstandard conditions in camera-based visiometers with two artificial black bodies are analyzed. The results show that the luminance contrasts of the two blackbodies are highly dependent on the environmental radiance distribution. The nonuniform sky illuminance can cause a large error in the blackbody contrast estimations, leading to substantial visibility measurement errors. A method based on a support vector machine (SVM) is proposed to classify the visibility under nonstandard conditions to ensure the reliability of the camera-based visiometer. A classification accuracy of 96.77% was achieved for the data containing images depicting different illumination conditions (e.g., a clear sky, cloudy sky, and overcast). The results show that the classifier based on the SVM is an effective and reliable method to estimate visibility under complex conditions.

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Section: Introductionmentioning

confidence: 86%

Error Analysis and Visibility Classification of Camera-Based Visiometer Using SVM under Nonstandard Conditions

Chen

Wang

et al. 2023

Atmosphere

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“…It fully uses the advantage of a single camera, but requires only the road and the sky in the scene. Based on Koschmieder's law, a mathematical model of contrast between a single black object and the sky background was established by Tang et al [17]. A new black target vision measurement system was introduced, which is simple in structure and does not require much calibration.…”

Section: Related Workmentioning

confidence: 99%

Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach

Chen

Qureshi

Geng

2022

IET Signal Processing

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The research of visibility detection in foggy days is of great significance to both road traffic and air transport safety. Based on the meteorological and video data collected from an airport, a deep Recurrent Neural Network (RNN) model was established in this study to predict the visibility. First, the Fourier Transform was used to extract feature variables from video data. Then, the Principal Component Analysis method was used to reduce the dimension of features. After that, 462 sets of sample data include image features, air pressure, temperature and wind speed, were used as inputs to train the RNN model. By comparing the predicted results with the actual visibility data as well as some other stateof-the-art methods, it can be found that the proposed model makes up for the deficiency of models based only on meteorological or image data, and has higher accuracy in different grades of visibility. With considering the meteorological data, the accuracy of RNN model is improved by 18.78%. Besides, with aids of correlation analysis, the influence of the meteorological factors on the predicted visibility was analysed, for fog at night, temperature is the dominant factor affecting visibility.

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“…For a well-maintained reference transmissometer, the accuracy mainly depends on the accuracy of the transmissivity measurement; therefore, differentiating equation ( 4) with respect to the transmissivity T yields [7]:…”

Section: Accuracy Of the Reference Transmissometermentioning

confidence: 99%

A method for calibrating forward scatter meters indoors

et al. 2020

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In this study, we developed a method for calibrating forward scatter meters (FSMs) against transmissometers for measuring visibility under laboratory conditions, and we designed and constructed a novel fog chamber for calibrating FSMs indoors. The fog chamber was used to simulate a controlled visibility environment, and a transmissometer was installed on both ends of the fog chamber as the standard. The developed method involved a comparison of the FSMs and transmissometer in a group of fog events. FSMs were installed on both sides of the optical path between the transmitter and receiver of the transmissometer. The chamber door was closed, and, after creating fog to simulate low-visibility conditions, measurements were collected from the FSMs and transmissometer every minute while the fog dissipated very slowly. The measurement errors of the FSM could then be calculated. To evaluate this method, tests were conducted in the fog chamber, and the results show that the fog chamber could simulate visibility from 10 m to 10 000 m, while the FSMs could only be calibrated below a visibility of 1500 m due to the baseline of the transmissometer. The fog homogeneity was controlled within 1.2% or less, and the stability was limited to 5% per 30 min. These parameters are reasonably homogeneous and stable for FSM-transmissometer comparison. Three PWD22 FSMs were calibrated using this method, and the calibration uncertainties were estimated to be in the range of 7.8% to 9.2%.

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A new visibility measurement system based on a black target and a comparative trial with visibility instruments

Cited by 19 publications

References 8 publications

Error Analysis and Visibility Classification of Camera-Based Visiometer Using SVM under Nonstandard Conditions

Error Analysis and Visibility Classification of Camera-Based Visiometer Using SVM under Nonstandard Conditions

Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach

A method for calibrating forward scatter meters indoors

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