## 5 Conclusions  
In this study, we argue that the use of visible images is more effective in tiny human target detection based on visual saliency in noisy maritime environments than that of infrared images. Sea surface sun glint can significantly deteriorate the performance of pixel-level detection algorithms, such as LCM-based algorithms. To this end, HDetect-VS is proposed to suppress strong noise and enhance the saliency of tiny human targets. The color characteristics of glint noise and objects, which are not taken into account in conventional pixel-level algorithms, are introduced into the HSV-gray method to suppress glint noise and retain the saliency of objects. The pixel intensity value distribution is presented as the pixel intensity surface calculated with large-scale Gaussian Convolution. For maritime objects with specific spatial distribution characteristics, two-step clustering can be used to separate tiny objects from sun glint and background noise. Moreover, the results of HDetect-VS can be used as prior knowledge in deep learning-based algorithms to significantly improve object detection performance. Experiments on the SeaDronesSee dataset showed that the proposed HDetect-VS method is robust and effective in maritime tiny human object detection. Conceptualization, Z.F., F.N. and J.S. ; methodology, Z.F. ; software, Z.F. ; validation, Z.F., Y.X. and D.D. ; formal analysis, Z.F. ; investigation, Z.F. and Y.X. ; resources, Z.F. ; data curation, Z.F. and D.D. ; writing--original draft preparation, Z.F. ; writing--review and editing, Z.F. ; visualization, Z.F. ; supervision, F.N., L.M. and J.S. ; project administration, F.N. and J.S. ; funding acquisition, F.N., L.M. and J.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{2}{c}{**Model**} & \multicolumn{4}{c}{**mAP**} & \multicolumn{4}{c}{**AP**} \\ \hline
**Experiment** & **Size** & **YOLOX** & **IoU: 0.5** & \
\begin{tabular}{c} **IoU:** \\ **0.5-0.95** \\ \end{tabular} & **Swimmer** & **Boat** & **Jet Ski** &
\begin{tabular}{c} **Lifesaving** \\ **Apiliances** \\ \end{tabular} & **Buoy** \\ \hline \multirow{4}{*}{**wo-Prior**} & \multirow{4}{*}{\(640^{2}\)} & s & 79.2 & 45.5 & 31.62 & 69.58 & 52.85 & 24.89 & 48.46 \\  & & m & 77.5 & 46.5 & 33.21 & 71.23 & 55.93 & 22.48 & 49.90 \\  & & l & 76.9 & 47.0 & 32.93 & 72.56 & 57.34 & 21.60 & 50.59 \\ \cline{2-10}  & & s & 82.7 & 50.7 & 37.10 & 74.05 & 56.33 & 29.27 & 56.66 \\  & & m & 81.0 & 50.9 & 38.35 & 74.47 & 56.94 & 26.18 & 58.38 \\  & & l & 81.3 & 51.3 & 37.96 & 75.41 & 58.28 & 27.34 & 57.33 \\ \hline \multirow{4}{*}{**w-Prior**} & \multirow{4}{*}{\(640^{2}\)} & s & 85.3 & 51.7 & 36.69 & 70.94 & 56.85 & 38.91 & 54.88 \\  & & m & 83.7 & 50.9 & 37.70 & 70.24 & 57.61 & 35.34 & 53.50 \\  & & l & 87.2 & 53.2 & 38.90 & 72.91 & 60.07 & 36.48 & 57.62 \\ \cline{2-10}  & & s & 87.1 & 54.7 & 39.41 & 72.52 & 59.33 & 42.86 & 59.55 \\  & & m & 88.1 & 54.8 & 40.00 & 72.68 & 58.61 & 43.78 & 59.11 \\  & & l & 89.3 & 56.2 & 41.12 & 74.28 & 60.46 & 45.16 & 60.15 \\ \hline \multirow{4}{*}{**w-Prior**} & \multirow{4}{*}{\(640^{2}\)} & s & 81.7 & 49.5 & 35.31 & 67.00 & 56.67 & 38.91 & 49.69 \\  & & m & 80.1 & 48.7 & 36.28 & 66.34 & 57.43 & 35.34 & 48.43 \\ \cline{1-1}  & & l & 83.5 & 50.9 & 37.44 & 68.86 & 59.88 & 36.48 & 52.17 \\ \cline{1-1} \cline{2-10}  & & s & 83.4 & 52.4 & 37.93 & 68.49 & 59.15 & 42.86 & 53.91 \\ \cline{1-1}  & & m & 84.3 & 52.5 & 38.50 & 68.64 & 58.42 & 43.78 & 53.52 \\ \cline{1-1}  & & l & 85.5 & 53.8 & 39.58 & 70.15 & 60.27 & 45.16 & 54.46 \\ \hline \end{tabular}
\end{table} 
Table 2: Deep learning method results of maritime object detection on prior results obtained with our methodology. Conflicts of Interest: The authors declare no conflicts of interest.