## 5 Discussion
### Limitations  
Overall, we focused on the application of exploration and engineering practice verification of a surface damage identification task of gray brick walls of traditional buildings in Macau based on the existing YOLOv8 model. Although this study has achieved certain interim results, there are still some shortcomings. First, in the setting of the label system, some damage categories (such as stains and damage, moss, and plants) have strong similarities in image representation, and the model is prone to confusion in actual recognition, especially in the color spot area produced after cement repair. Even manual recognition has subjective bias. Secondly, since the whole brick is used as the unit for labeling instead of the actual damaged area as the frame selection benchmark, there is a certain deviation between the thermal map response and the actual damage location. In addition, the model's  
Figure 15: Design and application of gray brick detection and identification device. (1) The structure of the device after opening; (2) The top view of the device; (3) The back design of the device; (4) The pull-rod design after the device is stored; (5) The physical prototype of the device will be exhibited at the 2024 Science and Technology Week and Innovation and Technology Achievement Exhibition in Macau. The Chinese in the picture is the exhibition board description of the 2024 Science and Technology Week and Innovation and Technology Achievement Exhibition. (Image source: drawn by the authors). recognition ability for the intact brick (intact) category is generally weak, probably because it lacks prominent visual features and fails to form effective classification features during training. Finally, the model's detection performance for long-distance images is poor, and it is not suitable for high-altitude shooting or drone collection scenarios. Compared with the innovative research on basic algorithms, the innovation of this paper is mainly reflected in the implementation of engineering applications, optimization of detection processes, and the establishment of a multi-dimensional evaluation system. This application research oriented to engineering needs is inevitably subject to the following limitations: (1) Due to the special requirements of the real-time and adaptability of algorithms in the field of architectural heritage protection, YOLOv8, which currently takes into account both accuracy and speed, was used as the core detection framework, and no horizontal multi-model comparison and theoretical breakthrough were carried out. (2) In the case of limited sample size and incomplete definition of multi-type labels, the current research was more focused on optimizing the application effect and engineering the feasibility of existing technologies in specific scenarios rather than proposing a new theoretical model or algorithm structure. (3) The diversity and complexity of the fields of architectural engineering and cultural heritage protection also limit the depth of algorithm innovation. We will further expand the sample, refine the label system, and explore more innovative solutions in combination with advanced detection algorithms. In addition, this study found that the model had some confusion when identifying certain similar categories (such as \"stain\" and \"damage\", \"moss\", and \"plant\"). This problem was reflected in the confusion matrix and heatmap results. The main reasons for this confusion are the blurred boundaries of label definitions, the visual similarities of image features, and the inconsistency of local annotations. Although this study has tried its best to ensure the quality of annotations and the balance of categories under the existing category system, it also fully recognizes the impact of category definition strategies on model performance.