## 4 Experiments
### Analysis of Experimental Results
In Table 2, we report the results obtained with **wo-Prior** for maritime object detection based on the multi-scale YOLOX model with different input image sizes. The variation in 0.5-0.95 mAP was about 0.5-1 points. By increasing the input image size from 640\({}^{2}\) to 1024\({}^{2}\), for different classes, AP increased by 4-8 points and mAP by 4 points. It can be noted that increasing the level of detail of objects is more conducive to performance improvement compared with increasing the size of the feature map based on a multi-scale model. Moreover, the improvement for tiny objects is much higher than that for medium and large objects. The performance ceiling for tiny objects for a certain input image size is much lower than that for medium and large objects. Therefore, increasing the level of detail of objects is essential for performance improvement in tiny object detection under conditions of a wide field of view. * Object detection on prior results based on YOLOX (**w-Prior**)  
The experiment was conducted based on the area results obtained with HDetect-VS (**w-Prior**), aiming to effectively increase the details of tiny objects. As shown for w-Prior in Table 2, maritime object detection performance based on the multi-scale model with different input image sizes showed significant improvements. The improvement for 640\({}^{2}\) images was much higher than that for 1024\({}^{2}\) images. The improvement in 0.5 mAP was relatively high: 6 points for the average and 10.3 points for the maximum. The improvement in 0.5-0.95 mAP was about 4-6 points due to the improvement for tiny objects, such as 13-17 and 2-6 AP points for the lifesaving appliances and swimmer classes, respectively. By contrast, the improvements in AP for the boat and jet ski classes were much lower. To comprehensively evaluate the effect of previously obtained area results on deep learning algorithms, the objects that could not be previously detected were considered, as shown in the results of **w-Prior Total** in Table 2. The improvement in object detection with 640\({}^{2}\) images was relatively high. The improvement in 0.5 mAP was about 2.5-6.6 points,and that in 0.5-0.95 mAP was about 2.2-4 points. The lifesaving appliances and swimmer classes were improved by about 13-15 and 3.1-4.5 AP points, respectively. The ADR for the boat class was lower than that for the other classes due to the use of the HSV-gray method, and the reduction for this class was about 3 AP points. Compared with the field and city environments, the maritime environment is more complex for tiny object detection tasks. In particular, the strong glint noise caused by light reflection significantly affects the imaging quality of and detection performance on tiny objects. To improve tiny human target detection performance under limited computing resources, deploying HDetect-VS in edge equipment on UAVs should be researched in the future. Different lighting conditions, variable flight paths, and flight attitudes might affect the robustness of the algorithm; thus, robustness improvement in real maritime scenarios under the above harsh conditions will be considered in our future work. Figure 8: Comparison of results of tiny object detection algorithms for maritime SAR. Yellow points in Glint Crop of input images represent locations of tiny objects.