# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 2 Related Work  
The core of the damage assessment process is the analytical assessment method used. In addition to manual interpretation, traditional methods mainly include Bayesian networks [4], principal component analysis (PCA) [5], and image feature understanding [6]. Evaluation methods based on Bayesian networks mostly rely on machine interpretation, which can synthesize various types of information and have the advantages of a short processing time and the ability to quantitatively analyze problems that are subject to uncertainty. [PERSON] et al. made full use of the advantages of Bayesian networks in computing uncertain problems to build a Bayesian model for target damage assessment. By using GeNIe software, this Bayesian model can assess the damage effect on target ships [7]. [PERSON] et al. used UAV reconnaissance technology to obtain image information of vehicle targets and analyzed the damage features using Bayesian networks to establish a functional damage assessment model [8]. By incorporating a dynamic Bayesian network analysis of a damage time stream, [PERSON] effectively overcame the subjectivity and uncertainty of traditional methods and achieved a higher assessment accuracy [9]. Nevertheless, although they are beneficial for solving uncertain problems, Bayesian-based evaluation methods cannot make use of human experience and knowledge and often cannot grasp principal contradictions. PCA-based methods mainly adopt the idea of dimensionality reduction to transform complex feature representations of images into only a few evaluation metrics, which are then used as quantitative features to evaluate the degree of image change [10]. [PERSON] et al. fused the advantages of PCA and synthetic aperture radar (SAR) imaging with multitexture feature extraction to evaluate the damage to ground buildings and reduce the amount of data computation [11]. [PERSON] et al. combined PCA with a convolutional mapping network to achieve change detection for high-resolution images acquired in different time periods [12]. [PERSON] and [PERSON] proposed an improved PCA-NLM method for urban development change detection based on SAR images from different time periods [13]. PCA-based methods can reduce the volume of data to be processed and capture principal contradictions. However, they are less robust to image interference. With the rapid development of remote sensing, image processing, and other related technologies, image-feature-understanding-based methods have become the focus of current research on damage assessment, which are generally divided into two categories: image-segmentation-based [14] and target-detection-based [15] methods. They are more widely adaptable, more resistant to interference, and more in line with human visual understanding than methods based on pixel and texture change detection. However, complex structures and uncertain environments make it difficult for the adaptability and accuracy of such assessment algorithms to meet the demands of application. The key to traditional damage assessment methods is the detection of changes in images acquired before and after a strike, which is essentially a feature extraction problem. Multiple errors are inevitably introduced when using various image processing algorithms, such as image preprocessing, image registration, and image radiation correction. At the same time, it is not easy to build an effective assessment model from a limited sample due to the specificity and confidentiality of the attacked targets. Moreover, such methods often rely on a control center, and the assessment results will inevitably have a certain lag, which is intolerable for some tasks with high real-time requirements [16]. Although much work has been done on image-change-detection-based damage assessment, these methods have seldom paid attention to the problems of achieving online and real-time performance. The emergence of learning-based methods provides a solution to these problems.