## 4 Experiments
### Analysis of Experimental Results  
\(\bullet\) Object detection based on visual saliency (HDetect-VS)  
The results of the experiment conducted on Object Detection V2, shown in Table 1, indicate that the ADRs for all classes and for tiny human targets were over 95% and over 96%, respectively, on both the training and validation sets. This indicates that the proposed method based on glint suppression and pixel surface can effectively enhance the saliency of objects. The density characteristics of maritime object distribution can be utilized to perform tiny human target detection. First, to achieve the suppression of sea surface sun glint, we applied the HSV-gray method to effectively suppress the high pixel intensity values for areas that presented white in the RGB images. However, since the boat objects in SeaDronesSee also have white \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & **Number of** & **Number of** & **ADR** \\  & **Ground-Truth Objects** & **Detected Objects** & \\ \hline Swimmer & 6206 & 5973 & 96.25\% \\ Boat & 2214 & 2091 & 94.44\% \\ Jet ski & 320 & 319 & 99.69\% \\ Lifesaving appliances & 330 & 330 & 100.00\% \\ Buoy & 560 & 507 & 90.54\% \\
**Val. total** & **9630** & **9220** & **95.74\%** \\ \hline Swimmer & 37,096 & 35,679 & 96.18\% \\ Boat & 13,022 & 12,104 & 92.95\% \\ Jet ski & 2330 & 2306 & 98.97\% \\ Lifesaving appliances & 923 & 920 & 99.67\% \\ Buoy & 4389 & 4237 & 96.54\% \\
**Train. total** & **57,760** & **55,246** & **95.65\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of tiny object detection based on visual saliency (Object Detection V2). Figure 7: The boxes obtained with HDetect-VS. The yellow points represent the locations of tiny objects (humans and boats in this figure). characteristics, their saliency was also slightly suppressed, resulting in a low ADR for boats. On the contrary, the color characteristics of lifesaving appliances and jet ski objects were much more defined, resulting in relatively high ADRs. Second, to comprehensively evaluate the effectiveness of HDetect-VS results as prior knowledge, as shown in Table 2, objects that fail to be detected should be considered in the result of the deep learning-based algorithm. The results of tiny object detection for SAR obtained with different algorithms are compared in Figure 8.  
Improved Local Contrast Measure (ILCM) [15], MS-AAGD, and HDetect-VS were used to perform tiny object detection in a normal maritime environment. The performance and edge quality of the objects detected with ILCM were better than the results of MS-AAGD and HDetect-VS. When sun glint appeared in an image, ILCM could not distinguish between glint noise and objects, and when the objects were concealed by sun glint, as in the **G**lint **images** in Figures 6 and 8, ILCM failed to detect them, generating false alarms. Compared with the ILCM algorithm, MS-AAGD slightly suppressed the dense glint in **G**lint **1** and **G**lint **2** in Figure 8. However, the effect of MS-AAGD on scattered glint suppression was comparatively small, as illustrated for **G**lint **3** in Figure 8. It should be noted that MS-AAGD is sensitive to the edge of tiny objects but not to their color characteristics, as illustrated for **G**lint **3** in Figure 8, in which the buoy was not detected. Our proposed HDetect-VS algorithm achieved tiny object detection in strong glint, as shown in Figures 6 and 8. This indicates that the performance of HDetect-VS is robust and effective for maritime SAR. The edge quality of the detected objects was slightly poorer compared with ILCM and MS-AAGD. However, in maritime SAR, it is more important to find tiny objects concealed by strong noise signals than accurately localize them. * Benchmark experiment of object detection based on YOLOX (**wo-Prior**)  
This experiment was conducted on Object Detection V2 (**wo-Prior**), aiming to provide a benchmark for object detection conducted on the results obtained with HDetect-VS.