## 4 Discussion  
### The Improvement Effect of Detailed Ground Objects on Detection and Its Limitations  
We used the typical target detectors Yolo and Faster RCNN as control experiments. Our method, based on the Faster RCNN algorithm, introduced the combined detection optimization method of construction project candidate box merge and detailed ground object combination modification. This paper classified construction projects according to their size and construction cycle, compared the detection results of this method and the Faster RCNN algorithm, and discussed the differences in detection results and the effect of detailed ground. The detection performance of this method in the low recall rate stage (left side of the PR curve) is significantly better than that of the Faster RCNN algorithm, that is, the false detection rate of this method at this stage is lower than that of Faster RCNN, but in the high recall rate stage (right side of the PR curve), the difference in detection performance is not significant. Figure 7 shows some of the detection results of the two experiments. The first two sets of detection results show that, due to the unclear boundaries of the construction project, although there is only one ground truth in the image, the Faster RCNN algorithm detected two high-scoring candidate boxes, resulting in poor results for both candidate boxes. The construction project candidate box merge can better address the feature of unclear construction project boundaries, making the detection results better match the ground truth, and lowering the false detection rate. Detailed ground objects usually have clear features and well-defined boundaries, so directly using Faster RCNN for detection will yield satisfactory detection results. When there is a certain detailed ground object box in the candidate box, the confidence of the candidate box will be improved, otherwise, it will be lowered, which is also the core idea of detailed ground feature combination modification in this method. In the third set of detection results, there exists false detection, and the confidence of the false detection box is high. Since there is no detailed ground object detected by the detailed ground object detector near the false detection box, it can be easily known from Equation (4) that the confidence of this candidate box in this method is lower than that in the Faster RCNN algorithm. The results of this set further show that for false detection boxes, the confidence of this method for such candidate boxes is lower than that of the Faster RCNN algorithm. Although this step cannot completely eliminate false detection results, it can limit the confidence of the false detection box and improve the confidence of the correct detection box to a certain extent, thus improving the detection performance as a whole. The introduction of detailed ground object detection is not only conducive to improving the confidence of conventional deep learning target detection methods, but also identifying the construction cycle and the implementation of soil and water conservation measures. In the first detection group in Figure 7 there is a large piece of bare soil (rock) in the construction project that can be detected through detailed ground object detection. Based on this, it can be judged that the construction project is in the stage of project leveling at the initial stage of construction. When detailed ground objects are detected in buildings under construction, the characteristics and completion of the building can be used to determine the type of construction project and whether the main body of the project is completed. At the same time, it can also judge whether soil and water conservation measures have been taken by the existence of the detailed ground object of a dust screen in the construction project.