## 4 Experiment and Results
### Experiment Design
#### 4.2.3 Metrics for Performance Evaluation  
In order to quantify the detection results of structural components in scanned 2D drawings based on YOLO, several commonly used metrics in the field of object detection such as precision (Pre.), recall (Rec.), missing rate (MR), F1 score (FS), overall accuracy (OA), and actual accuracy (AA) were utilized in this article. For one class of structural components, Pre. represents the ratio of the number of correct predictions to the total number of predicted components belonging to this class, while Rec. reveals the proportion of the number of correct predictions to the actual number of detected components in this class no matter whether the classification is right or not. These two metrics represent the discrimination ability of YOLO to negative samples and identification ability to positive samples, respectively. Normally, the detection results are expected with high Pre. and Rec. However, in fact, the components in scanned structural drawings in this work are not classified equally. For instance, the number of columns in CLPI is much more than other components, and the same thing happens for beams in FPI. This inequality may lead to the situation where one of these two metrics is high and another is low based on their definitions. Hence, FS was also adopted to evaluate detection performance of structural components. FS can be regarded as a weighted average of Pre. and Rec. It simultaneously accounts for both metrics and ranges between 0 and 1. The higher the FS is, the better the detection performance of YOLO will be. OA is the number of correct predictions to all predictions for this certain class of components. In contrast, AA stands for the number of correct predictions to the actual number of components that belong to this class. The definitions of these metrics are as follows:  
\[\text{{Precision}}\left(\text{{Pre.}}\right)=\frac{\text{{TP}}}{\text{{TP}}+ \text{{FP}}}\, \tag{6}\]  
\[\text{{Recall}}\left(\text{{Rec.}}\right)=\frac{\text{{TP}}}{\text{{TP}}+ \text{{FN}}}\, \tag{7}\]  
\[\text{{Missing Rate}}\left(\text{{MR}}\right)=\frac{\text{{FN}}}{\text{{TP}}+ \text{{FN}}}\, \tag{8}\]  
\[\text{{F1 Score}}\left(\text{{FS}}\right)=2\cdot\frac{\text{{Precision}}\times \text{{Recall}}}{\text{{Precision}}+\text{{Recall}}}=\frac{2\text{{TP}}}{ \text{{TP}}+\text{{FP}}+\text{{FN}}}\, \tag{9}\]  
\[\text{{Overall Accuracy}}\left(OA\right)=\frac{\text{{TP}}}{\text{{TD}}}= \frac{\text{{TP}}}{\text{{TP}}+\text{{FP}}+\text{{FN}}}\, \tag{10}\]  
\[\text{{Actual Accuracy}}\left(AA\right)=\frac{\text{{TP}}}{\text{{TA}}}\, \tag{11}\]  
where TP, FP and FN represent the number of true-positive, false-positive and false-negative detections for each class of components, respectively. Taking columns in CLPI as an example, according to the definitions of TP, FN and FP (shown in Table 3), TP is the number of columns that identifies correctly, FN is the number of columns that misidentifies as other types of components, and FP is the number of non-column components that misidentifies as columns. The reason why TN is not defined in this article is that for the detection process of a certain class of structural component, all regions that do not contain this type of component were negative, so it is meaningless to measure TN. TD is the total  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Total** & **Training** & **Testing** \\ \hline Original & 500 & 400 & 100 \\ Augmented & 1500 & 1200 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of training and testing images in original and augmented dataset.  
number of detections for this kind of component, while TA represents the actual number of components belonging to this class.