# Algorithm of Smart Building Supervision for Detecting and Counting Column Ties Using Deep Learning
## 6 Conclusions  
The conclusions obtained through the series of processes performed in this study are as follows:  
1. The possibility of smart detection of column reinforcement was seen by automatically classifying the column rebars and counting the number automatically. By using deep learning technology, it was possible to achieve the purpose of the study to classify and count the ties by separating the wall rebars and the column rebars among the numerous rebars at the construction site, and also by distinguishing the vertical and horizontal rebars from the columns;
2. By using the proposed technique in the supervision work, it was possible to perform the task of counting the ties, which is difficult to actually perform. In order to increase the versatility and accuracy of the built network, image pre-processing was performed. By obtaining a high accuracy of 95.24%, it was possible to see the possibility of utilization in the actual supervision work, and the possibility of improving the efficiency of the supervision work by obtaining a time-saving effect through the application of a process that could automatically check whether or not it matches the information of the BIM;
3. It was confirmed that by partially using it at the construction site, the time was reduced by more than a few tenths of that of a manpower. This will save more time and manpower if it is used in large buildings that take a significant amount of construction time;
4. The possibility of using BIM and deep learning convergence technology could be confirmed by suggesting a process of checking whether construction site information and BIM information match.  
This study was conducted for the purpose of increasing the efficiency of supervision work by utilizing the technology of deep learning, and it was possible to confirm the possibility of its use as a basic study of deep learning convergence technology in the BIM environment. Based on this possibility, we would like to suggest one step pre-processing programming and real-time drone object detection as future work to increase the usability as a real technology. Through the future work, this study is expected to be used in the real field. Therefore, it is considered to be research that has sufficient academic and practical development potential.  
[PERSON] conceived experiments, analyzed data, and wrote papers; [PERSON] investigated prior research and edited thesis; [PERSON] supervised the research. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22 AATD-C163269-02).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Location** & **X1Y1** & **X1Y3** & **X2Y1** & **X2Y3** & **X3Y1** & **X3Y2** & **Sum** \\ \hline DQ & 18 & 15 & 16 & 16 & 19 & 20 & 104 \\ MQ & 18 & 16 & 16 & 16 & 19 & 20 & 105 \\ Error & 0 & 1 & 1 & 2 & 0 & 1 & 5 \\ Accuracy (\%) & 100 & 93.75 & 93.75 & 87.5 & 100 & 95 & 95.24 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Error and accuracy of each column detection.