## 2 Materials and Methods
### Technical Process
#### 2.1.3 Post-Processing  
The post-processing is conducted based on the construction project detection results obtained by the detector. The visual differences within the construction project are huge, with inconsistent coverage and no clear and well-defined boundaries, making it difficult to obtain satisfactory detection results by directly using conventional deep learning target detection methods such as Faster RCNN. However, when the visual differences in the detailed ground object samples are small, with basically the same coverage, faster RCNN can be used to obtain satisfactory detection results. Therefore, it is necessary to improve the detection results based on the preliminary detection results, as well as the characteristics of the construction project and the detection results of detailed ground objects. The post-processing in this paper adopted two steps: (1) merging the candidate boxes of the construction project; and (2) modifying the detailed ground object combination. (1) Merging construction project candidate boxes. The preliminary detection results obtained by the construction project detector are multiple candidate boxes (hereinafter referred to as candidate boxes) and their corresponding confidence. Taking a construction project in Wuhan in Figure 3 as an example, based on the cognitive differences of different experts, the image can be identified as one construction project (a green box) or two construction projects (two red boxes). This difference in cognition will cause a large error in the target detection of construction projects, so this paper proposes a strategy of merging candidate boxes to mitigate the error. Figure 2: Schematic diagram of Faster RCNN network. In the construction project sample labeling, the green box in the above figure indicates the real situation on the ground, and the red candidate boxes are the detection result of the detector. The red candidate boxes overlap, so the detection results obtained by the construction project scene detector can be merged to match the ground truth (the green box). The specific implementation method is as follows: if the construction project scene detector detects that the candidate boxes A and B overlap, and the ratio of the overlapped area of A and B to the minimum area of the two is greater than the merging threshold \(\alpha\), and the confidence of both A and B is greater than the combined confidence threshold \(\beta\), then A and B will be merged into the candidate box C, the area of the candidate box C will be the smallest bounding rectangle of A and B, and the confidence of the combined candidate box will be:  
\[Conf=(Conf1*\text{S1}+Conf2*\text{S2})/\text{S3}, \tag{3}\]  
where S1, S2, and S3 are the areas of A, B and the merged candidate box C obtained from the construction project scene detection, and \(Conf1\), \(Conf2\), and \(Conf\) are the confidence of A, B, and C, respectively. (2) Detailed ground object combination modification. The difficulty of the detection of construction project scenes is reflected in the difficulty of identifying the range of candidate boxes, as well as the fact that the confidence of the detected candidate boxes is not accurate enough. The detailed ground objects have similar characteristics to general target detection categories, such as single features and clear boundaries. Therefore, the detailed ground object candidate box obtained by the detailed ground object detector (hereinafter referred to as the detailed ground object box) was used to assist the construction project scene detector, and the overall visual features of the construction project and the detailed ground objects inside were combined to obtain the comprehensive expression confidence of the construction project.