# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 5 Experimental Validation and Results
### Analysis of Experimental Results  
As for YOLO-VGGNet, the YOLO v3 detects, classifies, and localizes the targets in the images. To meet the requirements of real-time detection and timely damage assessment, the recognized image blocks containing the target are stored in a memory buffer. At the same time, another process is activated to input the image blocks into the VGG-1 series networks for damage level assessment. If the assessment result is functionally damaged, the image blocks will be further input into the VGG-2 network for damaged components analysis to maximize the use of computing resources. The time consumption for each stage of the YOLO-VGGNet damage assessment is shown in Table 3. The client received 15 frames per second through the UDP protocol in the actual test. Our hardware configuration is a Lenovo y7000p rescuer laptop, RTX2060 GPU, and 32 GB memory. The maximum processing speed is 21 frames per second. The corresponding convolutional feature clustering thresholds are set for the target's different components. Then, the missile swarm detects the ground target in real-time according to the predetermined trajectory, assesses the damage level, and analyzes the damaged components in a timely manner. In addition to PCA-k-means, we also compared our method with Mask-RCNN-based and Bayesian-Network-based methods on the basis of YOLO's prior information. The comparison of damage level assessment results is shown in Table 4. The number of targets for each damage level is 250 and the columns show the proportions of correct valuations. The recognition rate of YOLO-VGGNet, PCA-k-means, Mask RCNN, and Bayesian Network are 83.1%, 66.6%, 74.3%, and 67.1%. The experimental results show that the two-stage learning-based method YOLO-VGGNet achieves higher accuracy than traditional image-change-detection-based and single-stage learning-based methods. Although traditional methods evaluate faster and have a strong generalization ability to scenarios, they mainly focus on the physical and pixel spaces. The presence of thick smoke, sparks, and craters around the target can significantly impact the evaluation results. Moreover, the changes in the surrounding  
Figure 16: The result of PCA-k-means assessment. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} **YOLO Recognition** \\ **/Each Frame** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Damage Level Assessment** \\ **/Each Image Block** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Damaged Components Analysis** \\ **/Each Image Block** \\ \end{tabular} } \\ \hline Time/s & 0.03124 & 0.1985 & 0.5136 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time consumption for each stage of YOLO-VGGNet damage assessment. environment are not strictly related to the damage level and may come from previous strikes. In addition, it is concluded that single-stage damage level assessment suffers more from the influence of different inter- and intraclass features. Though the two-CNN learning-based method can extract deep semantic information, different destructive targets may exhibit the same damage characteristics due to the targets' different structural, functional, and anti-destruction characteristics. For example, sparks are fatal to tents but have little effect on tanks. Therefore, training a specific damage assessment model for each type of target is necessary. By specific-type evaluation models, Our two-stage learning-based method not only has the advantage of extracting deep semantic information but also avoids the influence of different inter- and intraclass features. Furthermore, existing methods can only get a rough functional damage assessment according to the damage level. Our method has a clear advantage over existing methods by focusing on the activation intensity of the clustered convolution kernels to determine whether the target's components are damaged or not, as shown in Table 5.  
turrets, tent structures, and supply vehicle trunks.