# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 4 Experimental Results and Analysis
### Analysis of Feature Visualization  
The deep CNN-based model is poor in explanation due to its black box working manner, and lots of work about feature visualization have been proposed to improve the interpretability of deep models. Class Activation Mapping (CAM) [16; 17; 18; 19; 20], i.e., heat map or salience map, is one of the most popular feature visualization techniques. The generated CAM map shows the importance of regions in the decision of deep models, of which the larger value means more contributions. The CAM methods can be roughly divided into the gradient-free method [16; 17; 18] and the gradient-based method [19; 20]. In this work, we took Grad-CAM [19] to generate CAM maps for exploring the mechanism of the proposed model, and examples of CAM maps are shown in Figure 13. It can be seen that the discriminant region is the turret; the difference in the turret among the three types of the tank is salient. Therefore, it can be seen as reasonable that the proposed model identifies tanks based on the turret, since it is similar to human beings. In addition, the discriminant region is very stable and was not affected by the changes in scene and object size. It can demonstrate that the model learned the distinguishing characteristics of objects, and the model is robust. The objects in the second, third, and fourth columns are smaller than that in the first column; we can see that the discriminant region in the second, third, and fourth columns are larger than that in the first column. As we know, the smaller object is more challenging to detect. Therefore, the model needs more information, and the discriminant region becomes larger to include more features. We can also see that the background of the fifth column is more complex than that of the first column, and the distinguishing area also becomes larger. Since the object located in the complicated background is also more challenging, the model also needs more information in the decision. It can be concluded that the model recognizes tanks by the turret, which needs more information on tanks with a small size and in a complicated background.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**AP (\%)**} & \multirow{2}{*}{**mAP (\%)**} & \multirow{2}{*}{**F1-Score (\%)**} & \multirow{2}{*}{**Times (s)**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{5-8}  & **Tank A** & & & & & & \\ \hline Faster-RCNN [4] & 84.05 & 72.22 & 64.85 & 73.70 & 74.40 & 0.319 & 3.14 \\ SSD [6] & 96.01 & 97.14 & 95.33 & 96.16 & 91.98 & 0.0245 & 40.8 \\ YOLOv1 [5] & 71.21 & 57.60 & 60.23 & 63.02 & 61.20 & 0.069 & 14.4 \\ YOLOv2 [7] & 80.40 & 72.50 & 79.64 & 77.52 & 76.35 & 0.050 & 20.1 \\ YOLOv3 [8] & 98.00 & 91.67 & 98.00 & 95.22 & 82.22 & 0.041 & 24.76 \\ YOLOv4 [9] & 96.75 & 95.20 & 97.33 & 96.42 & 95.05 & 0.046 & 21.3 \\ YOLOv5 & 97.10 & 97.28 & 97.40 & 97.16 & 96.16 & **0.020** & **50** \\ Proposed & **99.20** & **99.00** & **99.40** & **99.20** & **98.31** & 0.025 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the compared detection models. The bold part denotes the best performance.