## 2 Related Work
For this class of methods, the core is to design the number of features that can accurately assess the target, and the current research lacks the extraction of functional features of the target and cannot scientifically and accurately assess the state that the target is in. The second class of methods uses machine learning-type algorithms to build a damage assessment model based on existing data and to calculate the damage assessment results. [PERSON] et al. [19] combine the YOLO v3 target detection algorithm and a lightweight VGG network to achieve target detection and damage assessment of typical moving ground targets. [PERSON] et al. [20] use convolutional neural networks to generate building footprints from pre-hurricane satellite imagery and classify the damage caused. [PERSON] et al. [21] applied a genetic algorithm and BP neural network to construct a target damage tree and classify the damage level for damage assessment. [PERSON] et al. [22] proposed an assessment method based on a GA-dynamic BP neural network for the problem of radar position damage assessment and simulated the realization of the damage assessment by radar position. This type of method is improved in accuracy, but it requires a certain number of samples to construct a high-precision model in the specific implementation process, and it is not applicable to small-sample target damage assessment. For bridge targets, relevant studies at home and abroad are mainly divided into two aspects: damage effectiveness analysis and damage effect evaluation. The effectiveness analysis methods include the blockage algorithm for the bridge deck and simulation-based bridge state analysis research, and most of the application scenarios occur before the strike. The blockage algorithm for the bridge deck is a study of the bridge blocking effectiveness assessment model based on the probability model, using the Monte Carlo algorithm and other stochastic models of the bridge target for the strike simulation and then calculating the bridge blockage if there is a path through and other functional characteristics, finally using blocking probability or the probability of destruction as the destruction assessment index of the target bridge [23; 24; 25]. Based on the numerical simulation and the simulation analysis of bridge state analysis study, mainly by simulating the response and damage process of bridge structures in different strike situations, the mechanical changes after the strike are analyzed to provide a prediction of the destruction results for the actual strike situation [26; 27; 28; 29; 30]. The effect assessment method is mainly a study of the post-strike bridge destruction effect assessment method based on downward-looking images, and the application scenario is the assessment of the target status after the strike. With the development of technology, it is feasible and efficient to use images to analyze the condition of bridges. [PERSON] et al. [31] used a UAV to inspect two wooden bridges and automatically identify defects in the structures from the images collected. [PERSON] et al. [32] used a UAV and machine learning algorithms to perform an assessment of the condition of the bridges by recording damage information and visualizing it. Standard image processing methods, including edge following, image thresholding, and morphological operations, have been used in [33; 34; 35] to detect cracks in bridge decks. On the other hand, refs. [36; 37] built machine learning-based classifiers to form an automatic crack detection model. The analysis of the above literature reveals that it is possible to detect the state of bridges using images, but most of these studies focus on health monitoring and lack the assessment of the state of bridge damage under munitions strikes. For the assessment of bridge damage status under munitions strikes, [PERSON] [11] proposes a method to judge the truncation of bridges based on image change detection but can only judge whether the bridge is truncated or not in two states.