Research on Daytime Cloudiness Calculation for All-sky Camera Imagery Based on Deep Learning postprint

Abstract: Cloudiness is one of the important evaluation parameters for the site selection of ground-based photoelectric telescopes in astronomical field. The traditional cloudiness calculation method has a large deviation in the accuracy of cloudiness calculation for all-sky camera imagery, which is difficult to meet the actual demand for the accuracy of cloudiness calculation in multiple fields, and there are some limitations in its detection model extraction capability. Aiming at the problems of daytime cloudiness calculation of all-sky camera imag#2;ing, a deep learning-based daytime cloudiness calculation model of all-sky camera imaging is proposed. In the cloudiness detection layer, the model constructs a Channel Weighting#2;Feature Fusion (CWFF) structure to enhance the cloud memory and deep feature extraction capability to accomplish the cloudiness detection task. In the cloudiness calculation layer, the model proposes a cloudiness calculation method based on the cloudiness detection model, which effectively improves the error rate of cloudiness calculation. Experiments show that the combined accuracy of this paper’s method in the cloudiness detection task exceeds 95%, and the average absolute error in the cloudiness volume calculation task does not exceed 5%.