## 3 Sea Surface Sun Glint Suppression and Tiny Human Object Detection Based on Visual Saliency
### Sea Surface Sun Glint Suppression and Object Saliency Enhancement  
#### 3.2.1 Glint Suppression Based on HSV-Gray Method  
Because of the variable trajectory of UAVs, sea surface sun glint inevitably appears in visible images. To achieve object saliency enhancement, the high pixel intensity values of glint and sky should be suppressed based on the characteristics of pixel distribution in the RGB channels. Both glint and objects have high pixel intensity values on the grayscale, which cannot reflect the color characteristics of maritime objects. However, the difference in the pixel distributions of objects and glint noise in the RGB channels is significant. Specifically, in different RGB channels, a colored object has distinct pixel intensity values, while white glint presents no significant differences, with all values being close to 255. In the HSV (Hue, Saturation, and Value) model, based on human vision, different HSV channels numerically represent the color characteristics of objects. Therefore, the saliency  
Figure 2: The characteristics of pixel intensity value distributions of maritime UAV images. (**a**) Visible images and (**b**) 3D plots of pixel intensity values on the grayscale. of objects can be enhanced and that of glint noise can be effectively suppressed by using a grayscale mapped from the HSV model (HSV-gray). As shown in Figure 3, different colors have distinct values in the HSV channels. The values of white are zero in the H and S channels and maximum in the V channel, while the value of black is zero in all channels. Red and green have maximum values in the S and V channels and different values in the H channel because of the different color properties. We map the H, S, and V channels to the R, G, and B channels, respectively. HSV-gray is calculated according to the mapped HSV model as a common grayscale calculated based on the RGB model. On the grayscale calculated based on the RGB model (RGB-gray) and mapped HSV model (HSV-gray), the pixel intensity value of black is zero. The pixel intensity values of white are 9.69 in HSV-gray compared with 255 in RGB-gray. Red and green have much higher pixel intensity values compared with white in HSV-gray. Therefore, HSV-gray can be applied to enhance the saliency of objects concealed by the glint noise. The pixel intensity values are calculated based on HSV-gray, and the mapped results are presented as \(P_{\text{HSV-gray}}\). The relationship between \(P_{\text{HSV-gray}}\) and the pixel intensity values in different RGB channels is shown in Figure 4. Individual colors have distinct differences in pixel intensity values in different RGB channels. They present much higher \(P_{\text{HSV-gray}}\) values than the colors that have similar values in the RGB model. Therefore, glint with similar values in different RGB channels has a much lower \(P_{\text{HSV-gray}}\) value than objects with bright colors, which allows the saliency of the glint area to be suppressed. Figure 3: The calculation of HSV-gray according to the mapped HSV model. 3.2.2. Maritime Background Suppression Based on Large-Scale Gaussian Convolution  
Because of the view and trajectory variation of UAVs, the sea surface and sky, forming the background with high pixel intensity values, significantly influence the saliency of objects. To suppress this effect, the concept of pixel surface based on large-scale Gaussian Convolution was developed. Gaussian Convolution is the weighted average of the intensity of adjacent positions, with weights decreasing with the spatial distance from the center position (p). The influence that one pixel has on another thus depends only on their distance (\(r\)) in the image [52]. As shown in Equation (1), an image \(I\) filtered with Gaussian Convolution is given as \(GC[I]_{\text{p}}\), and the weight for pixel q is defined as \(G_{\sigma}(\mid\text{1 p}-\text{q}\mid\text{1})\) as follows:  
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