# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 5 Conclusions  
Using visibility equipment to assess fog conditions at sea can lead to errors due to the lack of visual information, and there are limitations in accurately understanding conditions in the middle of the ocean. To address this issue, this study proposes a method for the real-time monitoring of fog changes at low cost by applying the proposed algorithm to cameras installed on buoys or lighthouses at sea. The proposed AEA-RDCP algorithm aims to improve the accuracy of fog detection by minimizing the influence of atmospheric light.  
Existing RDCP algorithms estimate visibility by removing the sky region, but they fail to completely eliminate the effects of atmospheric light. AEA-RDCP overcomes these limitations by using the DCP algorithm to determine a threshold value for pixels influenced by atmospheric light and sets pixel values above this threshold to zero, effectively minimizing the impact of atmospheric light.  
The experimental results demonstrate that the AEA-RDCP algorithm provides superior fog detection accuracy in various marine environments compared to the RDCP algorithm, which only removes the sky region. This indicates that the proposed method significantly contributes to eliminating the influence of atmospheric light. For example, in images significantly affected by atmospheric light, the AEA-RDCP algorithm showed approximately 12% higher DC percentage values than the RDCP algorithm, proving that AEA-RDCP effectively reduces the influence of atmospheric light and enhances fog detection accuracy.  
In conclusion, the AEA-RDCP algorithm is an efficient method for accurately estimating fog intensity and visibility in marine environments at low cost. Traditional visibility equipment is expensive and requires high maintenance costs, whereas the camera-based approach provides reliable information from numerous marine activity points without additional costs. This is particularly beneficial for small fishing vessel operators, as it reduces economic burdens.  
However, the proposed algorithm still faces challenges in dark environments, such as evening or nighttime conditions [36]. In low-light conditions, achieving sufficient illumination can be difficult, reducing the accuracy of fog detection. Therefore, future research should explore integrating additional lighting devices or thermal cameras to address these limitations. Additionally, verifying the proposed algorithm under various weather conditions and marine environments is essential in order to contribute to the development of real-time marine safety systems.  
Conceptualization, T.-H.I; methodology, T.-H.I; software, S.-H.H.; validation, S.-H.H. and T.-H.I; formal analysis, T.-H.I.; investigation, S.-H.H.; resources, S.-H.H. and K.-W.K.;  
Figure 21: DCP percentage graph over time.  
data curation, S.-H.H.; writing-original draft preparation, S.-H.H.; writing-review and editing, T.-H.I.; visualization, S.-H.H.; supervision, T.-H.I.; project administration, T.-H.I.; funding acquisition, T.-H.I. and K.-W.K. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP)--Innovative Human Resource Development for Local Intellectualization program grant funded by the Korean government (MSIT) (IITP-2024-RS-2024-00436765). This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST), funded by the Ministry of Oceans and Fisheries, grant number 20210636.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.