# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 4 Experimental Results and Analysis  
### Experimental Setting and Model Training  
The experiments were performed on a graphics workstation equipped with an Intel Core I9-10900X CPU, 64 GB of RAM, and an Nvidia GeForce RTX 3080 Ti GPU with 12 GB memory. All three types of tanks in each scene were divided into training samples and test samples according to the ratio 2:1. The training samples and test samples of all scenes constituted a training set and test set. After the split, the samples of the training set and test set are 842 and 421, respectively. All of the test models were fine-tuned on the UAVT-3 dataset based on the weights provided in the original papers.  
In this work, Average Precision (\(AP\)), Mean Average Precision (\(mAP\)), and Frames per Second (\(FPS\)) were adopted as the evaluating metrics, which also have been widely used in object detection. \(AP\) refers to the area of the Precision (\(P\))-Recall (\(R\)) curve, which can be obtained by Equation (6)  
\[AP=\int_{0}^{1}PdR. \tag{6}\]  
\(mAP\) refers to the mean AP of all detection classes, which can be obtained by Equation (7)  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i}, \tag{7}\]  
in which \(N\) denotes the detecting classes, and \(N\) is 3 in this work. For both \(AP\) and \(mAP\), a larger value means better performance. \(P\) and \(R\) in Equations (6) and (7) can be obtained by Equations (8) and (9), respectively,  
\[P=\frac{TP}{TP+FP}=\frac{TP}{all\;dections}, \tag{8}\]  
\[P=\frac{TP}{TP+FN}=\frac{TP}{all\;ground\;truths}, \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Map** & \multicolumn{3}{c}{**Size of Anchor Box**} \\ \hline
160 \(\times\) 160 & (22, 74) & (32, 102) & (41, 55) \\
80 \(\times\) 80 & (48, 156) & (54, 89) & (76, 64) \\
40 \(\times\) 40 & (79, 121) & (95, 230) & (117, 156) \\
20 \(\times\) 20 & (161, 347) & (236, 195) & (255, 440) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The size of anchor boxes for the four feature maps.  
Figure 10: Clustering results. (**a**) Average IOU. (**b**) Visualization of the selected anchor boxes.  
where True Positives (\(TP\)), False Positives (\(FP\)), and False Negatives (\(FN\)) denote the number of correct detection boxes, false detection boxes, and missed detection boxes, respectively.  
In order to train the proposed model, we first pre-trained the model on the COCO dataset [15] for parameter initialization and then fine-tuned the model on UAVT-3. The training iterations of fine-tuning were set to 299, in which the best model was selected as the final model. Figures 11 and 12 show the training loss and detection accuracy in fine-tuning. Figure 11a-c show the training loss of the box regression, confidence, and classification. It can be seen from Figure 11 that all three types of losses decreased sharply with the iteration increase before 50 epochs, then changed slowly, and finally, transitioned to smooth.  
(mAP, F1-score) of YOLOv3, YOLOv4, and YOLOv5 is (95.22%, 82.22%), (96.42%, 95.05%), and (97.36%, 96.16%), respectively, all of which are higher than that of SSD (94.16%, 91.98%), and YOLOv5 reaches the highest (mAP, F1-score) (97.36%, 97.16%) among the comparison models. The (mAP, F1-score) of the proposed model reaches (99.20%, 98.31%), which has the best performance. The proposed model yields an increase of (1.84%, 2.15%) compared with the original YOLOv5 in (mAP, F1-score), of which the AP of the three tanks exceeded 99%. The improvements in the model in mAP and AP are due to the modifications on YOLOv5. In the computation complexity, YOLOv5 has the best performance since it has the lowest time per image (0.020 s) and the largest FPS 50. The proposed model needs 0.025 s per image, and its FPS is 40, which has a slightly higher time complexity. However, it is enough for real-time detection.