# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 1 Introduction  
Damage assessment of targets based on images has been a significant problem in intelligent military perception. The U.S. Joint Chiefs of Staff defined the Battle Damage Assessment (BDA): The timely and accurate estimate of damage resulting from the application of military force, either lethal or non-lethal, against a predetermined objective [1]. This work focuses on the damage assessment for mobile ground targets, such as tank, supply vehicle, and tent. Unlike traditional fixed ground targets in previous works, mobile ground targets are more flexible. They are moving on the ground for certain specific tasks and their positions are constantly changing in real-time. Therefore, the damage assessment method for mobile ground targets should also operate in real-time and only on the UAV's hardware, without the participation of a rear control center. However, since most of the existing works evaluate the damage status of the target by detecting the changes in images before and after a strike, real-time and online damage assessment is nearly impossible for them. Specifically, they are often divided into three parts: register the images before and after the strike, detect the struck target from the images, and assess the damage level based on the changes in the geometric and texturefeatures [2]. Since the high-precision registration of the images from before and after the strike is a complex and time-consuming process, it is always performed offline at the control center rather than online on only the UAV's hardware. The traditional image-change-detection-based methods are mainly for fixed targets. Since they need high-precision registration of the images from before and after the strike, they are unavailable for mobile ground targets. Although learning-based methods can extract deep semantic information and detect mobile ground targets in real-time, they often suffer more from the influence of different inter- and intraclass features. Therefore, these existing methods are hard to extend to mobile targets. Moreover, most existing works focus on the damage level assessment for the whole targets, lacking of further status evaluation for their functional components. However, such information is also very important for the evaluation of the various specific abilities of mobile ground targets. For example, a supply vehicle with destroyed wheels will no longer be able to move, making it easier to be attacked. A similar work can be found in [3], which analyzed the structure of typical fixed ground target based on expert knowledge and realized a functional damage assessment. However, it is hard to extended to various mobile ground targets. Due to the stationary nature of each part of a fixed target, there is a stable position relationship between the key components and the overall body of the target. In contrast to the case of a fixed target, the position relationship of a mobile target will change significantly with the target's movement and the observation angle. In addition, methods of functional damage assessment based on expert knowledge are often designed for large-scale general targets and require a large amount of data maintenance and management. Applying such methods to mobile targets is difficult due to the wide variety of mobile targets and their complex structural variations. To tackle the above problems, we propose a real-time detection and timely damage assessment method named YOLO-VGGNet for mobile ground targets. Firstly, with YOLO v3 as the precursor, the proposed method detects, classifies, and localizes mobile ground targets accurately and quickly. Secondly, the image blocks of detected targets are fed into the lightweight VGG networks, which output the coarse results of damage level assessment. Later, based on class activation mapping (CAM) and deconvolution, we further analyze the activation intensity of clustered convolution kernels and determine whether the targets' key components are destroyed.