# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 4 Experiments and Evaluations
### Dataset without the Influence of Atmospheric Light: Performance Comparison and Evaluation with RDCP  
In this study, experiments were conducted using Dataset (a), which consists of images classified by fog intensity and is not influenced by atmospheric light, to compare the performance of the existing DCP-based fog intensity measurement algorithm with the proposed AEA-RDCP algorithm. The dataset used for the experiments comprises a total of 320 images, each with a resolution of 1280 \(\times\) 720 pixels. Before comparing the fog detection performance of the two algorithms, we first observed how the DC percentage values change when the sky region is removed from sea images to assess the impact of external light sources. Figure 13 is an example comparing the fog intensity levels in images from four regions (Baengnyeongdo, Ganghwa-do, Pyeongtaek Dangjin, and Jido) when the sky region is not removed (a) versus when it is removed (b). The graph specifically shows data from the Baengnyeongdo region. The x-axis represents the number of images, and the y-axis represents the DC percentage values for each image. Here, the DC percentage value denotes the ratio of DC image pixels below the threshold value. In Figure 13, Graph (a) shows that, on clear days, the average DC percentage value is approximately 62%, which is the highest value observed. In contrast, Graph (b) shows that the average DC percentage for images on clear days is close to 100%. This result indicates that, by removing the sky region, the ambiguity in the DC values of the image is eliminated, suggesting that images with the sky region removed are less affected by external light sources. Therefore, the DC percentage values below the threshold, determined using fog intensity measurement algorithms that remove the sky region, can be more clearly distinguished according to fog intensity, and it is sufficient for estimating fog intensity in images without atmospheric light influence. \begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **DCP Percent (\%)** \\ \hline Dataset (a) & 63\% \\ Dataset (b) & 40\% \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of DCP percentages in original images using example images. Figure 12: Example images of clear days from the datasets in Figures 9 and 10: (**a**) image on a clear day without atmospheric light interference; and (**b**) image on a clear day with significant atmospheric light interference. The main goal of the experiment is to assess the effectiveness of the proposed AEA-RDCP algorithm in measuring fog density in images without atmospheric light influence. To do this, the experiment compares the DC percentage values obtained from images with the sky region removed and images processed with the AEA-RDCP algorithm, evaluating the performance of both algorithms. The experiment utilized Dataset (a), collected from four different regions to minimize the influence of atmospheric light. In each trial, the DC percentage values from images with the sky region removed were compared with those from images processed by the AEA-RDCP algorithm. Pixels exceeding the threshold for atmospheric light estimation were set to zero and excluded from the DC distribution calculation, ensuring more accurate fog density measurements. The interpretation of the results is presented through Figures 14-17. In these figures, Graph (a) represents the results for images with the sky region removed, while Graph (b) shows the results after applying the AEA-RDCP algorithm. The comparison of the two graphs reveals that the DC percentage values align with the criteria outlined in Table 1 and are nearly identical for both algorithms. This similarity is attributed to the minimal influence of external light sources, such as sunlight reflections, in Dataset (a) from the four regions. Therefore, it is confirmed that the AEA-RDCP algorithm is just as effective as the RDCP algorithm in measuring fog density in images without atmospheric light influence.