RDCP: A Real Time Sea Fog Intensity and Visibility Estimation Algorithm

Abstract

A number of accidents at sea are primarily caused by low visibility due to sea fog. Therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. Visibility meters utilize optical sensors rather than visional information, so that the measured visibility data occasionally includes errors. Moreover, visibility meters have significant costs so that it is not viable to install them at various locations. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior), which provides reliable estimation at a low cost by processing images captured from cameras, since they receive identical information as human eyes. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in Republic of Korea are used. Considering facilities in oceans, which are usually operated in power-limited environment, the processing time performance of PDCP is also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time.

FogFusionNet: Coastal Sea Fog Prediction by Using a Multimodal Deep Learning Approach

“…First, some studies [8], [9], [10] used observational data from geostationary weather satellites such as the Geostationary Ocean Color Imager (GOCI), Geostationary Operational Environmental Satellite, and Himawari. Second, other studies [11], [12] used time series observation data, whereas [13] employed Light Detection And Ranging data, [14] used closed-circuit television (CCTV) images to predict sea fog, and [15] used CCTV images to estimate the intensity of sea fog.…”