# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 4 Experimental Results and Analysis
### Further Discussion  
The UAV images were usually degraded by fog blur and motion blur, which not only affect the visual quality but also affect the accuracy of deep models since the existing  
Figure 13: Class Activation Maps from UAVT-YOLOv5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Blur Type** & **Blur Degree** & **Models** & **P (\%)** & **R (\%)** & **mAP (\%)** \\ \hline \multirow{4}{*}{Fog} & \(\sigma=0.004\) & \(M_{na}\) & 86.6 & 85.0 & 90.7 \\  & \(\sigma=0.004\) & \(M_{a}\) & 92.8 & 98.6 & 99.0 \\  & \(M_{na}\) & 84.1 & 70.0 & 78.2 \\  & \(M_{a}\) & 93.8 & 98.6 & 99.1 \\  & \(M_{na}\) & 83.7 & 58.0 & 66.7 \\  & \(M_{a}\) & 98.2 & 99.2 & 98.8 \\  & \(M_{na}\) & 91.5 & 87.4 & 91.8 \\  & \(M_{a}\) & 91.6 & 98.9 & 99.1 \\  & \(M_{na}\) & 80.0 & 69.5 & 75.0 \\  & \(M_{a}\) & 90.8 & 99.1 & 99.1 \\  & \(K_{m}=100\) & \(M_{na}\) & 69.5 & 42.9 & 51.3 \\  & \(M_{a}\) & 93.9 & 98.2 & 98.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation Results on Fog and Motion Blur Images. deep models were usually trained on high-quality samples. Training deep models on degraded samples is an effective way to improve the performance. However, degraded images are difficult to collect. Enhancing the image quality by a pre-processing method is another effective way to improve the image quality and performance of deep models. We test homomorphic filter [21] and FFA-Net [22] to pre-process the fog-blurred images and DeblurGAN [23] to pre-process the motion-blurred images. Figure 14 shows the pre-processed results, where (a) and (d) are the fog-blurred and motion-blurred images, (b) and (c) are the pre-processed images by homomorphic filter and FFA-Net, respectively, and (e) is the de-blurred images by DeblurGAN. It can be seen that the visual quality of the fog-blurred and motion-blurred images was improved obviously. Then, we evaluated the proposed UAVT-YOLOv5 trained on the free-blurred training set on the pre-processed images from the blurred test images of UAVT-3. Figure 15a,b show the results of fog-blurred and motion-blurred images, P-Hom and P-FFA-Net denote homomorphic filter and FFA-Net pre-processing in Figure 15b, P-Meblur denotes DeblurGAN pre-processing in Figure 15b, P-Ma and P-Mna denote data augmentation of fog blur without any pre-processing and data augmentation in Figure 15a and in Figure 15b. It can be seen that the mAP of P-Hom and P-FFA-Net is higher than that of P-Mna, which means the pre-processing by the homomorphic filter and P-FFA-Net improved the performance. However, the mAP of P-Hom and P-FFA-Net is still lower than that of P-Ma, especially when the image has a heavy degradation. The same phenomenon can be seen from the motion blur shown in Figure 15a. It can be concluded that the accuracy from pre-processing was improved and deserves further study on pre-processing methods aiming for performance improvement. Figure 14: Pre-processed blurred images. (**a**,**d**) are the fog-blurred and motion-blurred images, (**b**,**c**) are the pre-processed images by the homomorphic filter [21] and FFA-Net [22] respectively, and (**e**) are the de-blurred images by DeblurGAN [23]. In splitting the UAVT-3 database into training and test sets, Scene-based Splitting (SS) and Random Splitting (RS) schemes were tested. In SS schemes, the samples of five scenes were selected as the training set and the rest as the test. Two SS schemes were tested, the training and test scenes of the SS1 scheme are (GG-I, WL-I, WL-II, WL-III, Bush-I) and (Bush-I, GG-II). In the SS2 scheme, the training and test set are (GG-I, WL-I, WL-II, Bush-I, GG-II) and (WL-III, Bush-I). In the RS scheme, all of the samples in UAVT-3 were divided into training and test sets in a random way at a ratio of 2:1. The experimental results are shown in Table 4. It can be seen that the mAP of SS1 and SS2 are 26.5% and 65%, respectively, which are very slow. Moreover, the mAP gap between SS1 and SS2 is very large. It can be said that the accuracy is related to the division of the scenes.