## 2 Literature Review
### Development of Equipment for Identifying Surface Damage  
The construction of non-destructive testing models requires long-term investigation, collection, debugging, and optimization and also requires improvement through continuous testing. At present, cutting-edge research results are no longer limited to the analysis of laboratory workstations but have been further deepened to independently develop mobile identification devices or systems [21; 33]. For example, some scholars have used the CPU of a smartphone to process the video captured by the phone's camera in real time and used the TensorFlow Object Detection API to integrate the trained model into an Android-based smartphone. Finally, they verified the detection performance of the smartphone through two cases: detecting the walls of the Forbidden City and detecting the moat walls [21]. They achieved real-time detection of brickwork damage using smartphones. In addition, some scholars have developed the GreatWatcher system based on MCS technology and deep learning algorithms to monitor damage on the surface of the Great Wall [33]. Table 2 shows the mobile devices developed by scholars for identifying damage on Chinese gray brick surfaces in existing research. It is clear that creating mechanical equipment using computer vision models for non-destructive testing involves some costs, but its portability significantly enhances where and how the model can be used, and it also has potential economic value in the market. In addition, scholars from Xi'an University of Architecture and Technology proposed an ancient building gray-brick damage detection algorithm that integrates an attention mechanism in response to the research of our team in 2023 [5]. They made improvements through the YOLOv8n algorithm: for example, adding shallow feature map inflow to the feature fusion network to solve the problem of low recognition accuracy of low-level features such as small cracks and damaged textures in damage detection; introducing the Convolutional Block Attention Module (CBAM) in the feature fusion layer to achieve dual attention to channel features and spatial features, thereby improving the algorithm's ability to fuse features of different dimensions; in the feature extraction network part, the C2f module is improved through Shuffle Attention (SA) to solve the problem of feature information loss caused by deep convolution dimensionality reduction operations, thereby further improving the performance of the model [34]. Their improved model has achieved overall improvements, but they also found that the detection accuracy of the damage type of moss attachment is at an average level, and the improved algorithm has no significant improvement over the original algorithm. The reason may be that the size of the annotation boxes of this type of damage instance varies greatly, and there is a clear gap in the characteristics of the moss's survival and death states. In past research by scholars, it has been verified many times that the training set is the basis of model learning, and its quality and quantity directly affect the performance of the model. Therefore, researchers tried to expand the scope of collecting images and increasing the number of labels to create datasets. This will help the research team to further improve the model and deepen this research based on previous studies.