Research on visibility detection model optimization based on dark channel prior and image entropy and visibility development trend prediction

Abstract

Abstract In this paper, a visibility detection model based on the dark channel prior and image entropy is established to improve the lane line detection algorithm. Our algorithm does not need to preset the target, nor is it affected by the camera calibration parameters and position. It transforms the visibility calculation problem into the atmosphere transmittance calculation problem and refines the required results through the guided filter, achieving more accurate and stable visibility estimation results. In addition, based on the changing regularity of the visibility over time obtained by the detection model, a mathematical model is established to predict the change of heavy fog. We use ADF to test the visibility obtained in the visibility detection model and calculate the autocorrelation and partial autocorrelation functions. Finding the original sequence non-stationary, we perform the difference on the data, remove all insignificant factors and then incorporate the data into ARIMA model for fitting, finally getting the fitting and prediction results. The results are found similar to the actual situation, indicating that the results obtained by the visibility prediction model are robust and reliable.