# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 4 Experiments and Evaluations
### Dataset Affected by Atmospheric Light: AEA-RDCP Performance Evaluation  
This experiment's primary objective was to assess the effectiveness of the proposed AEA-RDCP algorithm, which sets pixels influenced by atmospheric light to 0, compared to the RDCP algorithm, which removes the sky region, in accurately measuring fog density in images significantly affected by atmospheric light. The experiment was conducted using Dataset (ab), which consists of images with a resolution of 1280 \(\times\) 720 pixels and is sensitive to atmospheric light interference. To evaluate the performance of each algorithm, the experiment focused on estimating the proportion of DC image pixels below a threshold value of 100. Figure 18 presents the results for 20 images within the dataset, showing the DC percentage values for both the RDCP and AEA-RDCP algorithms. The RDCP algorithm  
Figure 16: Image set without the effect of atmospheric light—graph comparison between crop image and excluded image (Ji Island): (**a**) Crop Image; and (**b**) Crop Image/Excluded Airlight. Figure 17: Image set without the effect of atmospheric light—graph comparison between crop image and excluded image (Ganghwa Island): (**a**) Crop Image; and (**b**) Crop Image/Excluded Airlight. Figure 15: Image set without the effect of atmospheric light—graph comparison between crop image and excluded image (Pyeongtaek): (**a**) Crop Image; and (**b**) Crop Image/Excluded Airlight. primarily removes the sky region to mitigate atmospheric light effects, whereas the AEA-RDCP algorithm further reduces these effects by setting the estimated atmospheric light pixels to 0. This comparison highlights the strengths of each approach in managing images with significant atmospheric light interference. When examining the graphs, it is clear that the DC percentage values below the threshold are generally higher in images processed with the AEA-RDCP algorithm compared to those where only the sky region was removed. Specifically, the maximum DC percentage for the sky region removal method is approximately 90%, with a minimum of 63%, and an average of 81.6%. In contrast, the AEA-RDCP algorithm achieves a maximum DC percentage of nearly 100%, a minimum of 74%, and an average of 94%. This indicates that the AEA-RDCP algorithm improves the DC percentage value by about 12% on average compared to the RDCP algorithm. Additionally, Figure 19 provides a detailed analysis by selecting the image with the most significant difference in DC percentage between the two methods. This figure illustrates (a) the original DC percentage, (b) the DC percentage after removing the sky region, and (c) the DC percentage after removing both the sky region and mitigating the effects of atmospheric light. This comparison visually emphasizes the impact of each processing step and shows that the AEA-RDCP algorithm offers more accurate fog density estimation in images influenced by atmospheric light. Figure 19a illustrates how, without preprocessing, the DC percentage value significantly decreases due to the influence of external light sources, even on a clear day. However, in Figure 19b, after removing the sky region, the DC percentage value increases by more than 40%, indicating a substantial improvement in visibility. Finally, when the atmospheric  
Figure 19: Comparison of graphs of images with strong effects of atmospheric light: (**a**) Original Image, (**b**) Crop Image, and (**c**) Excluded Airlight. Figure 18: Image set with the effect of atmospheric light: graph comparison between Crop Image and Crop Image excluding atmospheric light. light pixels are set to 0, the DC percentage approaches nearly 100%. This outcome demonstrates that the AEA-RDCP algorithm effectively mitigates the influence of external light sources, enhancing the accuracy of fog detection while reducing computational complexity.