# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 4 Target Damaged Components Analysis Based on Convolutional Feature Clustering
### Deconvolution Feature Visualization Analysis  
Deconvolution is the reverse operation of convolution. The concept of deconvolution was initially applied in signal processing techniques. Encouraged by its great progress in signal processing, deconvolution has been widely used in various fields. The deconvolution originates from Ref. [30], in which [PERSON] et al. firstly visualized the features to enhance the interpretability of convolution kernels. The \"deconvolution\" in our paper refers to the deconvolution process. It refers to the backpropagation of the features extracted in an intermediate convolutional layer to the original pixel space, which is beneficial for analyzing features extracted by convolution kernels. As shown in Figure 5, no new parameters are learned in the deconvolution process, which is unsupervised. It is well known that neural networks consist of multiple layers, each containing numerous neurons. For example, each CNN layer contains hundreds or thousands of convolution kernels. Different convolution kernels are trained to extract different features of a target. However, for different targets or different target's components, only a small number of convolution kernels will be activated in response. Deconvolution visualization can establish the connection between the feature map of the intermediate layers and the original image. Although the features extracted by the convolution kernels can be analyzed visually and graphically, no one knows at which layer and by which convolution kernels extract the needed features. Therefore, we propose a new convolutional feature clustering method by combining the advantages of CAM and deconvolution. It first selects the convolutional layer according to the CAM, which contains the richest feature information of the target's components, and then clusters the convolution kernels with large responses to the target or the target's specific components by deconvolution. Figure 4: Score-CAM feature visualization for different targets. Figure 3 reveals that the features extracted by the 26 th layer of convolution have better discrimination of the target's components. Therefore, we analyzed 512 feature maps of this layer (The size of each feature map is 14 \(\times\) 14). Herein, we introduce the maximum activation visualization method. It generally finds the maximum activation value in each feature map, then compares the maximum activation values of all feature maps in this layer and selects the largest one as the maximum activation unit. Finally, it sets all parameters in this layer to zero except for the maximum activation unit and maps the maximum activation unit to the original pixel space by deconvolution. The principle is shown in (8). \[Deconv(\overset{512}{MAX}(\overset{196}{MAX}(x_{ji}))), \tag{8}\]  
where \(x_{ji}\) indicates the unit in the 26 th convolution layer, \(j\) indicates the number of feature maps, and \(i\) indicates the unit number in each feature map. As shown in Figure 6, the target's components corresponding to the maximum activation unit are the tank turret and radar bracket. This method is useful for analyzing the extracted features of the convolution kernels, but it is one-sided. Only comparing the maximum activation values of the feature maps cannot represent the true activation area of the 196 units of each feature map, since the activation values for areas without targets are negative and cannot all be summed up. Considering the proportion of the actual target in the image, we add a filter \(\delta\) to remove the noise from irrelevant areas of the image and only keep the areas with large response values. The sum of the first 36 activation units is taken as the activation intensity of this feature map. Meanwhile, different convolution kernels may extract features of the same target or the same target's component. We convert (7) to (9). The improved maximum activation visualization method is shown in (10). It realizes the feature visualization extracted from any single convolution kernel by deconvolution.