# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 4 Target Damaged Components Analysis Based on Convolutional Feature Clustering
### Feature Visualization Based on CAM  
A total of 1200 images were collected for VGG-2 with four types of targets, of which 1000 were used as training sets and 200 as the test set. In addition, we do not need damaged training data, all belong to \"normal\". The training parameters and the loss function are the same as those for the VGG-1 series.  
Based on the gradient information, the CAM is backpropagated from the result to the intermediate convolutional layer. Then, the weights of all feature maps in the layer are summed and mapped onto the original image in the form of a heatmap, as shown in (6).  
\[L_{CAM}^{c}=\sum_{i=1}^{n}w_{i}^{c}A^{i}, \tag{6}\]where \(n\) is the number of feature maps, \(A^{i}\) is the \(i\)th feature map in the convolutional layer, and \(w^{c}_{i}\) is the weight of the \(i\)th feature map when the classification result is \(c\). Global average pooling (GAP) ensures that the number of feature maps matches the weight parameters. From the optimization perspective, this equation can be converted into (7).  
\[L^{c}_{CAM}=ReLU(\sum_{k}\alpha^{c}_{k}A^{k}_{l-1}), \tag{7}\]  
where \(\alpha^{c}_{k}=W^{c}_{l,l+1}|k|\), and \(W^{c}_{l,l+1}|k|\) is the weight of the \(k\)th neuron of layer \(l\) after GAP. The application of the \(ReLU\) activation function prevents the disappearance of the gradient, but there is still a partial loss. The CAM image is generated by upsampling to scale up \(L^{c}_{CAM}\) to the input image's size and superimpose it on the original. Three activation mapping visualization methods, Grad-CAM, Score-CAM, and Layer-CAM, were selected to analyze the features extracted from the intermediate VGG convolutional layers. The ablation experiment was performed to compare their feature visualization effect. The radar is taken as an example, as shown in Figure 3.  
The low and middle convolutional layers in Grad-CAM have poor feature visualization because of the loss of gradient information when backpropagating. Layer-CAM significantly improves due to a smaller loss of gradient information when backpropagating. Score-CAM gets rid of the dependence on the gradient and the final result is obtained from a linear combination of weights and activation mapping, which best visualizes the features extracted at the targets' components.  
It is concluded that Score-CAM has the best feature visualization of the target's components. We select the convolutional layer with the richest weight information based on Score-CAM. Moreover, this characteristic corresponds to the VGG's loss function based on human eye perception. Since the low convolutional layers mainly extract simple and intuitive features, while the middle and high layers are richer in semantic information, the focus is on visualizing the middle and high layers. The feature of the target's different components can be separated during the processing, and we only visualize the weight information using Score-CAM. As shown in Figure 4, different targets have different weighting components, mainly distributed in tent structures, tank turrets and armor, supply vehicle heads and trunks, and radar brackets and antennas.  
Figure 3: The comparison of different CAM visualizations.  
The advantage of feature visualization based on CAM is that the gradient information relying on the convolution process is simple and intuitive, and the weights of the target's components are comprehensible. The disadvantage is that the presented heat map is the accumulation of the weights of all convolution kernels in this layer. We cannot analyze the convolution kernels with different degrees of activation separately.