# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 2 Related Work
In recent years, convolutional neural networks (CNNs) have achieved great success in computer vision and pattern recognition. In tests on many large datasets, deep CNNs have exceeded human recognition rates and have a faster detection speed [17]. Considering the advantage of extracting deep advanced semantic features through learning based on large amounts of data, the application of learning-based methods for damage assessment has also attracted widespread attention [18; 19; 20]. [PERSON] et al. proposed a new baseline dataset for road damage detection with attention learning. Their dataset was collected by professional onboard cameras and is manually labeled with eight damage categories of three degrees (mild, moderate, and severe), which can effectively help promote research on the automated detection of road damage [21]. [PERSON] et al. applied a Mask-RCNN to evaluate the abrasion areas of vehicles involved in road traffic accidents to reduce the workload of insurance company personnel [22]. Damage assessment based on a single-stage CNN requires considerable calculation for each image. When faced with a large amount of data that need evaluation, this approach is very time-consuming. Therefore, many researchers have used lightweight networks to first determine whether a target is damaged to improve the evaluation efficiency. [PERSON] et al. designed a two-CNN disaster assessment network to evaluate the extent of the damage inflicted on coastal cities and structures. Two neural subnetworks are applied to evaluate whether houses are affected by floods and the damaged components of the houses [23]. In the same way, [PERSON] et al. designed a two-CNN learning-based damage assessment method for mechanical structures. Two subnetworks were built to evaluate whether a structure is damaged and the degree of damage [24]. The two-CNN evaluation approach significantly improved the accuracy and computational efficiency. However, although the existing methods based on two-CNN can achieve improved evaluation efficiency and obtain better results by extracting deep semantic features, the objects of evaluation belong to a single target type. The relationship between the two CNNs in the processing approach is relatively weak. How do we evaluate the damage degrees of different types of targets? Is such a two-CNN evaluation method effective for this purpose? Some researchers have considered judging the target type based on the first subnetwork. To evaluate the damage degrees of different houses after a typhoon, [PERSON] et al. combined two CNNs. The first subnetwork was used to distinguish different types of houses and localize their positions, and the second was used to evaluate the degree of damage to each house based on prior information from the first CNN [25]. Similarly, to evaluate the degrees of damage caused by different natural disasters, [PERSON] et al. concatenated two CNNs to classify the disaster type and damage level [26]. However, the relationship between the two CNNs was still weak, depending only on classification information. We propose a strongly concatenated two-stage learning-based method for mobile ground targets based on prior YOLO detection, classification, and localization information. Despite the undeniable progress achieved by learning-based damage assessment approaches in recent years, the explainability of deep neural networks presents an unavoidable problem. Due to the lack of analysis of the intermediate results, it is difficult to give concrete reasons for the experimental results obtained, thereby limiting the further applications of such learning-based approaches, especially in the fields of automatic vehicle driving and medical disease diagnosis. Therefore, many existing works have focused on the operating mechanisms and feature extraction logic of CNNs, a topic known as explainable AI (XAI) [27]. By visualizing the features extracted from intermediate convolutional layers, the study of feature visualization can effectively improve the understanding of XAI [28].