Aiming at the visibility prediction problem that is currently concerned in the transportation and aviation fields, an effective detection method based on complex network theory is proposed. Firstly, preprocess the images in the FROSI data set to obtain the gray image after the background difference subtraction, and derive and calculate the transmittance. Secondly, a corresponding mathematical model based on a complex network is proposed. After three steps of gray contour extraction, recognition parameter extraction, and image recognition and classification, a simulation platform is built and canny operator is used to complete image edge feature contour extraction. Finally, the processed image is combined with the formula to detect the visibility change trend in different time periods and compare it with the true value for comparison, the corresponding evaluation results are obtained from the relative error analysis.
Visibility prediction is a concern issue in the field of public transportation, which is related to the normal operation of flights and the safe travel of vehicles. Therefore, reasonable prediction of visibility is very important to improve the efficiency and safety of public transportation. This paper mainly combines data and video, and images are quantified for quantitative analysis of large fog evolution trends. Further, the mathematical model is established to study the visibility prediction problem that is concerned with the current traffic, aviation field, and proposes targeted recommendations for the current predictive means. Preprocessing the sample data, eliminates the wild value, performs interpolation processing on the missing position, regression analysis of the image, obtains the regression model of image visibility changes, performs visibility prediction, select accuracy, adaptive ability, good depth integration Convolutional Neural Network (CNN) is an algorithm for learning processing on the image. In the model establishment, the three image processing modes (Fourier change algorithm, spectral filtering, original pictures) are established, and the hidden feature, depth integrated volume is established by the three image processing modes (Fourier change algorithm, spectral filtering, original pictures). Total Neural Network (CNN) simultaneously learns three images of the input and generates a classification output, and finally the categorized visual network model.
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