# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 3 Target Detection and Damage Level Assessment Based on YOLO-VGGNet
### Target Damage Level Assessment Based on VGG-1  
Based on the prior detection, classification, and localization information of targets by YOLO v3, the image block \(G_{b_{m}}\) of the \(m\)th detected target could be cropped from the image \(G\) and then fed into the VGG-1 series networks to assess the target's damage level. Considering the huge differences among different types of ground targets, four class-specific assessment models are designed for four types of targets. According to the classification of image blocks \(C_{m}\), the corresponding VGG-1 network is selected: VGG-1-1, VGG-1-2, VGG-1-3, and VGG-1-4 for the tank, radar, supply vehicle, and tent, respectively. Then, the damage level assessment problem can also be simplified as a classification problem. There are many other classification networks with deeper layers, stronger feature representation capabilities, and higher recognition accuracy than VGG, such as ResNet-18 and Inception v3. However, VGG is a classical serial CNN without a residual structure, which means that the extracted feature map contains the target's position information. For example, the upper left corner of the feature map strictly corresponds to the upper left corner of the original image. Therefore, by means of weight visualization, CAM can visualize the regions in the feature map that the neural network pays attention to, meaning that the results of VGG are highly interpretable. For each types of ground targets, they can be divided into different damage levels. In this work, four types of targets are labeled as four damage levels: _normal, slightly damaged, functionally damaged_, and _severely damaged_, as shown in Figure 2. _Normal_ is for the target that has not been struck. _Slightly damaged_ means that the functions of the target are not affected. _Functionally damaged_ targets lose one or more key components. _Severely damaged_ indicates that the target has been totally destroyed and lost all of its key components. As a classification problem, the loss function only contains the classification error, which is shown in (4). \[L_{2}=\frac{\|\varphi_{l}(y)-\varphi_{l}(y^{\prime})\|_{2}^{2}}{C_{l}H_{l}W_{l}}, \tag{4}\]  
where \(y\) is the image's label, \(\hat{y}\) is the predication of the VGG-1 network, and \(\|\varphi_{l}(y)-\varphi_{l}(y^{\prime})\|\) is the error between the classification and the target label. \(\varphi\) indicates the loss network of VGG, \(C_{l}\) indicates the number of layers of the network, and \(C_{l}H_{l}W_{l}\) indicates the feature map size of layer \(l\). VGG uses the perceptual loss function based on human eye perception, which is different from the earlier \(L2\) loss function based on image pixel space. The \(L2\) loss function does not match the image quality and features perceived by the human eye, while the space calculated by the perceptual loss function is converted from image space to feature space. Such a loss function setting has the advantage of a more understandable feature extraction, which can effectively extract the target's advanced semantic features and exclude irrelevant factors' interference. The VGG-1 series networks can be described by:  
\[\phi_{2}^{C_{m}}(G_{b_{m}},\theta_{pre^{\prime}})= \begin{cases}0,&\text{normal },\\ 1,&\text{slightly damaged},\\ 2,&\text{functionally damaged},\\ 3,&\text{severely damaged}.\end{cases}  \tag{5}\]  
Figure 2: Supply vehicles with different damage levels. From (**a**–**d**), the damage levels of supply vehicles are normal, slightly damaged, functionally damaged, and severely damaged, respectively. \(\phi_{2}^{Cm}(\cdot,\theta_{pre^{\prime}})\) indicates the VGG-1 network of category \(C_{m}\) and \(\theta_{pre^{\prime}}\) is its weights, \(G_{b_{m}}\) is the output of YOLO v3. The output contains four damage levels: normal, slightly damaged, functionally damaged, and severely damaged.