## 2 Materials and Methods  
### Technical Process  
This method was inspired by semantic event detection in video data in the field of computer vision. It has always been challenging to detect semantic events with complex definitions (such as weddings, gatherings, etc.) from videos, which are characterized by unclear definitions, large intra-class differences, and the inclusion of complex visual features [16; 17; 18; 19; 20]. Studies found that the events in videos can be composed of more specific underlying concepts; for example, wedding events can be composed of wedding dresses, cakes, MCs, and other concepts that are easy to detect by target detection [21; 22; 23; 24]. Thus, this method corresponded the complex events of construction projects to the complex events in videos, and the various types of detailed ground objects in the scene to the underlying concepts in the video. It further used the target detection method and the combination of detailed ground objects in the complex scene to improve detection results.  
This process is divided into the following steps: (1) Construction of sample data set; (2) Selection and expression of detailed ground objects; (3) Target detection of construction projects and detailed ground objects; (4) Post-processing. The flow chart is shown in Figure 1, with the red boxes highlighting the innovation points of this paper.  
2.1.1 Selection and Expression of Detailed Ground Objects  
For the complex semantic scenes of a construction project, it is necessary to find out which detailed ground object can enhance its detection, that is, to find the type of detailed ground object that can best represent the construction project. The selection is made based on expert experience and the area and quantity of various detailed ground objects in the construction project sample.  
It is assumed that there are some types of common detailed ground objects in the area where the sample is located. However, with the large number of types of detailed ground objects, and limited information provided by some objects, the use of low-informative detailed ground objects will make it even more complex. Therefore, it is necessary to select certain types of detailed ground objects which can provide the corresponding amount of information for identifying complex semantic scenes of construction projects. The information amount of the \(i\)th detailed ground object can be calculated by the following equation:  
\[C_{i}=N_{i}*w_{i}, \tag{1}\]  
where \(N_{i}\) is the amount of detailed ground objects in all sample boxes of the construction project. Then, \(w_{i}\) can be calculated according to the following equation of term frequency-inverse document frequency (TF-IDF) [25; 26]:  
\[w_{i}=TF_{i}*IDF_{i}=\frac{N_{i}}{N}*lg\frac{Y+1}{Y_{i}}, \tag{2}\]  
where \(TF_{i}\) is the frequency of occurrence for the \(i\)th detailed ground object, and its value is the ratio of the number of its occurrence in the construction project label box to the number of its occurrence in all labeled boxes. The inverse document frequency, \(IDF_{i}\), is the ratio of the average area of construction projects in the sample area, \(Y\), to the average area of the \(i\)th detailed ground object in the sample area, \(Y_{i}\) (taking logarithm for calculation). (Note: In Equation (2), the denominator of \(lg\frac{Y+1}{Y_{i}}\) is taken as \(Y+1\) to ensure that the calculated \(IDF_{i}\) will be greater than 0, and the effect on the actual result can be neglected.)  
Figure 1: The method flow chart.