## 4 Experiment and Results
### Experiment Results  
The detection performance for each type of component is shown in Table 4. It can be seen that grid references have the best results with Pre. of 86.41%, Rec. of 99.16% and FS of 92.35%. The reason for this situation is that grid references appear most frequently in both CLPIs and FPIs, and their features are more distinctive than other components. On the other hand, horizontal beams are identified with the lowest Pre. of 82.67%, Rec. of 91.18% and FS of 86.71% as well as the highest MR of 8.82%. It demonstrates that beams, especially referring to those placing horizontally on 2D drawings, cannot be accurately recognized by YOLO in some cases. For example, when the aspect ratio of the beam is too large, the horizontal beam is often neglected to detect. When the aspect ratio is small, this type of beam is easily identified as a vertical beam. A horizontal beam tends to be identified as a column if its aspect ratio is close to 1. In addition, it also can be observed from Table 4 that the Pre. of all components is lower than their Rec. According to the definitions of precision and recall (Section 4.2.3), this result can be attributed to the fact that FP is always greater than FN for each type of component. In other words, other components are frequently misidentified as this kind of component during the process of object detection.  
In addition to the aforementioned metrics, OA, AA and speed of component detection by YOLO are also evaluated in this experiment. It can be seen from Table 5 that grid references and vertical beams have the highest OA and AA, while horizontal beams perform the worst in these two metrics. Moreover, the total averages of OA and AA for the whole image rather than for one type of component are calculated as comprehensive performance indexes to measure the detection results of the image when using YOLO. Since large differences between the total amount of each class exist (for instance, the number of grid references is about 20 times than that of sloped beams), weighted average method instead of arithmetic average method is applied in this study to obtain these two image-level accuracies. The equation for calculating these two metrics is \((x_{1}N_{1}+x_{2}N_{2}+\ldots+x_{k}N_{k})/k\), where \(x_{i}\) (\(i=1,\,2\,\ldots\,\,k\)) represents the OA or AA for each class of component, and \(N_{j}\) (\(i=1,\,2\,\ldots\,\,k\)) denotes the actual total number component of each class. Referring to Table 5, the total average of OA reaches 83.31%, with 81.25% of the components in image being correctly recognized. Also, the average computing time for an image is 0.71 s. These results demonstrate that the proposed YOLO-based method has the ability to detect multiple structural components from structural 2D drawings with comparatively high accuracy and a short detection time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **TP** & **FP** & **FN** & **Pre.** & **Rec.** & **MR** & **FS** \\ \hline Grid & 2967 & 467 & 25 & 86.41\% & 99.16\% & 0.84\% & 92.35\% \\ Column & 833 & 158 & 17 & 84.03\% & 98.04\% & 1.96\% & 90.50\% \\ Beam & 517 & 108 & 50 & 82.67\% & 91.18\% & 8.82\% & 86.71\% \\ Beam\_v & 500 & 100 & 42 & 83.33\% & 92.31\% & 7.69\% & 87.59\% \\ Beam\_s & 125 & 17 & 8 & 88.24\% & 93.75\% & 6.25\% & 90.91\% \\ \hline \hline \end{tabular} Note: Grid, Column, Beam, Beam\_v and Beam\_s in the table denote grid references, columns, horizontal beams, vertical beams and sloped beams, respectively.  
\end{table}
Table 4: Detection performance for different objects in test set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Predicted Objects** & **Actual Objects** \\ \hline TP & \(\surd\) & \(\surd\) \\ FP & \(\surd\) & \(\times\) \\ FN & \(\times\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definitions of TP, FP and FN for component detection.