# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 2 Related Work
Feature visualization has functions similar to those of image feature extraction based on computer image processing [29]. If we could utilize such visualized features, this would be beneficial in enhancing the ability of CNNs to solve damage assessment problems. Convolutional feature visualization methods generally include intermediate feature layer visualization [30], class activation mapping(CAM) [31], and deconvolution [32]. In CAM, the importance of each component of an image to the final decision is represented by generating a heatmap as an intuitive representation of the weight distribution. [PERSON] et al. proposed Grad-CAM to make CAM more universal [33]. Grad-CAM optimizes the backpropagation process and calculates the weights corresponding to each feature map in a convolutional layer by performing global averaging of the gradients and summing the weights. Based on Grad-CAM feature visualization, [PERSON] et al. introduced feature information to evaluate the environmental damage after a typhoon [34], and [PERSON] explained the results of building damage classification and derived the image basis for the neural network's determinations [35]. They both obtained better damage assessment results by introducing XAI theory into the damage assessment field. Not only Grad-CAM but also other works on XAI-based feature visualization have enabled significant progress. [PERSON] et al. increased the credibility of diagnostic results based on XAI [36]. [PERSON] et al. modified a dataset and training model based on XAI and achieved a better post-disaster building damage assessment effect [37]. Nevertheless, the current XAI-based methods also have the shortcoming that they cannot work in a real-time and online manner. The reason is that the neural network structure is relatively complex, and considerable weight information is lost during backpropagation, leading to high time consumption during processing. The development of XAI theory not only increases trust in the results generated by AI systems but also provides new assistance in solving existing problems. In deconvolution visualization [32], a feature map is taken as the input to perform the inverse of convolution, thereby mapping the results to the original pixel space to display the features extracted by the convolution kernels of each layer. The latest research on the visualization of convolutional features provides novel approaches to damage assessment problems. LayerCAM [38] can produce reliable class activation maps from any CNN layer by employing element-level weights. Score-CAM [39] gets rid of the dependence on gradients by obtaining the weight of each activation map through its forward passing score on the target class. The final result is obtained by a linear combination of weights and activation maps, which achieves better visual performance and fairness for explaining the decision-making process. To combine the advantages of learning-based and XAI-based methods, we propose the YOLO-VGGNet method. It can not only meet the requirements of real-time detection and timely damage assessment online but also fully utilize the visualized features from the convolutional layers.