# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 4 Target Damaged Components Analysis Based on Convolutional Feature Clustering
### Convolutional Feature Clustering for Target-Damaged Components
#### 4.3.2 Analysis of Damaged Components Based on Convolutional Feature Clustering  
We input image blocks of the radar into the VGG-2 network to analyze the damaged components and take the radar bracket and the antenna as an example for testing, as shown in Figure 12.  
The principle of convolution feature clustering is shown in (13). The changes in activation intensity of the clustered convolution kernels for radar's components are shown in Figure 13.  
\[\phi_{3}(G_{b_{m}},\theta_{pre^{\prime}})=\sum_{p=1}^{10}\epsilon_{pq} \tag{13}\]  
where \(\phi_{3}(\cdot,\theta_{pre^{\prime}})\) indicates the VGG-2 network and \(\theta_{pre^{\prime}}\) is its weights. If the result of \(\phi_{2}^{C_{m}}(G_{b_{m}},\theta_{pre})\) is functionally damaged, the image block \(G_{b_{m}}\) will be input to VGG-2 network. \(\sum_{p=1}^{10}\epsilon_{pq}\) is the sum activation intensity of the clustered convolution kernels. \(q\) indicates the different components of the target.  
As shown in Figure 13, the activation intensity of the clustered convolution kernels decreases significantly after the strike. Therefore, we can set a threshold, and the sum of the activation intensity of the clustered convolution kernels can be used as a condition to determine whether the target's components have been destroyed. The images of the target before and after the strike are shown in Figure 14. The clustered convolution kernels for the targets' different components are shown in Table 2.  
Figure 11: Frequency distribution of the target’s different components. (**a**) Radar antenna. (**b**) Radar bracket.  
Figure 12: Sample of damaged components. (**a**) Damaged bracket. (**b**) Damaged antenna. (**c**) Intact.  
We take the tank turret, the tent structure, and the supply vehicle trunk, as examples. The changes in the activation intensity of the clustered convolution kernels before and after the strike are shown in Figure 15.  
\begin{table}
\begin{tabular}{c c} \hline
**Different Components** & **Feature Maps of the Top 10 Activation Intensity (from Large to Small)** \\ \hline Tank (turret) & 268, 31, 22, 474, 421, 342, 289, 353, 174, 115 \\ Tank (tread) & 115, 31, 268, 174, 22, 146, 214, 79, 421, 219 \\ Radar (antenna) & 443, 232, 12, 384, 341, 409, 300, 371, 293, 498 \\ Radar (bracket) & 378, 31, 214, 115, 268, 22, 211, 299, 125, 148 \\ tent (structure) & 101, 283, 350, 179, 343, 504, 443, 120, 202, 317 \\ tent (texture) & 443, 202, 101, 283, 379, 433, 135, 269, 350, 276 \\ Supply vehicle (trunk) & 491, 152, 219, 366, 227, 423, 268, 450, 180, 31 \\ Supply vehicle (head) & 423, 296, 219, 31, 22, 447, 115, 210, 416, 366 \\ \hline \end{tabular}
\end{table}
Table 2: Clustered convolution kernels for the targets’ different components.  
Figure 14: Image changes before and after the strike.  
Figure 13: Changes in the activation intensity of clustered convolution kernels before and after the strike. (**a**) Radar bracket. (**b**) Radar antenna.