## 3 Experiments
### Experiments of Scene Understanding Module  
To evaluate the performance of the scene-understanding algorithms, comparative experiments were performed among ResNet-101 [29], AlexNet [48], Inception-v3 [49], and VGG-19 [50]. All of the algorithms were tested using the public Places365 dataset [51], which covers 365 scene categories with 10 million images. All of the algorithms were trained with 9 million samples from the Places365 training dataset and tested with 1 million samples. Table 7 presents the comparative experimental results among the four scene-understanding algorithms, where VGG-19 was superior in Top-1 and ResNet-101 was the best classifier in Top-5. According to the flight strategy of Table 1, we further categorized the 365 classes into 6 classes, the experimental results of which are displayed in Table 8.  
According to Table 8, ResNet-101 outperformed the other scene-understanding algorithms when the 365 image classes were further clustered into six classes as per the flight strategy described in Table 1. Meanwhile, we also established our own test dataset by collecting 160 real aerial images covering six  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Gestures** & **P** & **HP** & **HK** & **VP** & **VK** & **R** \\ \hline FPN [44] & 0.78 & 0.77 & 0.80 & 0.74 & 0.82 & 0.77 \\ \hline RefineDet [45] & 0.78 & 0.81 & 0.79 & 0.83 & 0.79 & 0.85 \\ \hline Faster RCNN [31] & 0.60 & 0.61 & 0.58 & 0.69 & 0.65 & 0.57 \\ \hline Handpose [46] + Resnet101 [29] & 0.71 & 0.75 & 0.68 & 0.69 & 0.79 & 0.71 \\ \hline Yolo-v3 [27] & 0.81 & 0.85 & 0.76 & 0.83 & 0.82 & 0.86 \\ \hline Our algorithm & **0.97** & **0.97** & **0.94** & **0.98** & **0.95** & **0.96** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recall Rate of different algorithm when Precision is 0.98.(trained and tested by our datasets).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Gestures** & **P** & **HP** & **HK** & **VP** & **VK** & **R** \\ \hline FPN [44] & 0.71 & 0.65 & 0.62 & 0.66 & 0.70 & 0.75 \\ \hline RefineDet [45] & 0.78 & **0.88** & 0.75 & 0.78 & 0.79 & 0.74 \\ \hline Faster RCNN [31] & 0.62 & 0.58 & 0.65 & 0.55 & 0.65 & 0.63 \\ \hline Handpose [46] + Resnet101 [29] & 0.75 & 0.84 & 0.67 & 0.72 & 0.69 & 0.66 \\ \hline Yolo-v3 [27] & 0.84 & 0.86 & **0.89** & 0.82 & 0.85 & 0.80 \\ \hline Our algorithm & **0.85** & 0.83 & 0.88 & **0.92** & **0.86** & **0.88** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Recall Rate of different algorithm when on the NYU test data when precision is set as 0.98. (“P” represents gesture Paper, “HP” represents gesture Horizontal Palm, “HK” represents gesture Horizontal Knife, “VP” represents gesture Vertical Palm, “VK” represents gesture Vertical Knife, “R” represents gesture Rock).  
Figure 21: The RPC analysis results based on our own dataset.  
types of scenes, namely indoor, factory, square land, water surface, and buildings. Figure 22 illustrates several scene classification results of ResNet-101 over the real aerial images captured from our UAV. In our dataset, the classification accuracy of ResNet-101 was 96.25%, which was quite close to its performance in the public dataset.