# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 1 Introduction  
Maritime accidents can occur due to various causes, but those caused by fog are particularly frequent. Low visibility conditions, where visibility decreases to less than 1 km, are one of the main causes of maritime accidents [1, 2]. Sea fog is a common type of mobile fog that occurs when warm, moist air moves over a colder surface or body of water. This phenomenon changes rapidly and occurs locally, appearing and disappearing quickly, which makes it difficult to accurately assess conditions in specific areas. These characteristics emphasize the importance of real-time monitoring to ensure maritime safety [3, 4, 5].  
Visibility measurement equipment used to assess fog intensity is essential for ensuring maritime safety [6]. This equipment uses optical sensors that emit infrared light and measure the amount of light scattered by aerosols and the atmosphere. However, such equipment is quite expensive. Additionally, visibility data obtained at sea can contain significant errors. Consequently, visibility data are recommended to be measured by well-trained observers following the guidelines of the World Meteorological Organization (WMO) [7]. In South Korea, there are 23 observer measurement centers and 291 meteorological measurement systems in operation [8]. According to a 2018 study, comparing datacollected by trained observers and visibility meters showed that visibility meters detect fog more frequently, with errors being more pronounced at sea [9]. For example, measured data sometimes show abrupt changes in fog intensity, which does not align with the typical gradual thickening or thinning of fog. Such issues can lead to conflicts of interest, such as frequent fog disrupting vessel operations and affecting the livelihoods of small fishing vessel operators [10].  
Advancements in fog density estimation and real-time monitoring technologies are crucial for preventing maritime accidents and ensuring the safety of the marine environment. Recent studies have focused on using video-based algorithms to detect fog conditions instead of traditional visibility meters [11]. These video-based fog intensity measurement algorithms utilize cameras to capture marine images, enhancing accuracy by excluding sky regions that could interfere with fog detection. By applying the Dark Channel Prior (DCP) technique [12; 13], these algorithms improve computational efficiency by calculating the dark channel ratio within the image, enabling the rapid real-time estimation of fog intensity. This approach helps monitor locally occurring fog conditions in real time, reduces costs, and enhances maritime navigation safety by preventing accidents.  
However, estimating fog intensity using images alone presents several challenges. For example, while the human eye can distinguish between sunlight reflections on the water and actual fog on clear days, video-based algorithms may mistakenly classify bright regions caused by sunlight or atmospheric scattering as fog [14]. To address this issue, this paper proposes a new fog intensity measurement algorithm, AEA-RDCP, which removes the influence of atmospheric light (such as sunlight) to enable accurate fog detection even in images with severe atmospheric scattering. Additionally, the study employs two datasets to estimate sea fog density under various conditions: one consisting of images unaffected by atmospheric light and another heavily influenced by it. The proposed algorithm offers the same cost-effectiveness as existing video-based fog intensity measurement algorithms (RDCP) while improving accuracy and reducing the risk of accidents due to incorrect detection.  
This paper is organized as follows: Section 2 reviews the relevant literature focusing on the DCP technique and introduces the existing DCP-based fog intensity measurement algorithm (RDCP). Section 3 explains the details of the AEA-RDCP algorithm. Section 4 compares the performance of AEA-RDCP and RDCP and presents the experimental evaluation results of the AEA-RDCP algorithm.