## 6 Conclusions  
In recent years, BIM has been continuously regarded as a core tool to operate and manage the as-built and as-is buildings. However, the lack of BIM models in existing buildings limits BIM applications in O&M phase. Building 3D models based on 2D engineering drawings is regarded as a feasible way to reconstruct BIM models. As the first step of this kind of method, the accuracy of component detection from 2D drawings and the quality of extraction of corresponding geometric information have a significant impact on the final results of reconstruction. Therefore, in this paper, a novel component detection method based on YOLO was proposed to quickly and accurately identify structural components in scanned structural drawings. The experimental results show that Pre. and Rec. for all five classes are above 80% as well as the average of OA and AA for the entire image. Moreover, the average time for identifying an image is only 0.71 s. All these results strongly prove the feasibility and potential of the proposed YOLO-based method for recognizing components from 2D structural drawings. The main contribution of this study is that it opens up a new way to detect structural components for reconstructing a BIM model of existing buildings from paper-based 2D drawings. Also, it is of important referential value for the application of the deep learning-based component detection method in other professions like architecture and mechanical systems. The second contribution is that it clearly \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Correct** & **Localization** & **Background** & **Similarity** & **Classification** & **Missing** \\ \hline Average & 69.54\% & 10.09\% & 8.03\% & 5.65\% & 2.26\% & 4.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Proportion of different types of errors across five classes of components. certifies the advantages of the proposed method over traditional component recognition methods. Compared with the existing approaches mentioned in Section 2.1, YOLO-based method not only guarantees the accuracy of components detection, but also has more advantages in detection speed and cost. What is more, the method proposed in this paper has the trait of universality due to its powerful learning ability, which means it can detect components in scanned 2D drawings generated in different countries and design conventions if the training data is sufficient. The final contribution of this study is that several optimal experiment settings have been found for better performance of the proposed YOLO-based method, though there is still room for improvement. It offers some references for the following researchers to improve the work mentioned in this paper. Additionally, the method proposed in this study can be further improved. The future research work is concluded as follows:  
* The experiment of component detection in this paper is focused on limited structural components (e.g., beam and column). To further improve the proposed method, more structural elements (such as floor, shear wall and pile) need to be tested in future research. * More advanced YOLOv2 with batch normalization, high resolution classifier, anchor box, and multi-scale training will be tried. In addition, more sensitivity analysis will be conducted to study the influence of various accuracy-influencing factors on the final recognition results. * Last but not least, as the first step to reconstruct BIM models for existing buildings, this study only solves the problem of extracting geometric information of structural components. How to extract topological and semantic information and match with the corresponding components will be the key research direction in the future. Conceptualization, Y.Z. and X.D. ; methodology, Y.Z. ; validation, Y.Z. and H.L. ; writing--original draft preparation, Y.Z. ; writing--review and editing, Y.Z. ; visualization, Y.Z. ; supervision, X.D. and H.L. ; project administration, X.D. ; funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript.