## 4 Experiments
### Discussion  
The evaluation of the different models and datasets helps in answering the stated research question (see Section 2.3). The first part of the question was successfully answered by the developed procedure, especially through the synthetic generation of the training data. For the second question part, the obvious answer is that the data are more essential for the object detection, since without them no training would be possible. But, this answer is quite simplistic and does not reflect the whole truth. In general, the results show that the dataset has a higher influence on recognition accuracy than the model, because with the third and fourth SketchGraph dataset it was possible for the YOLO and RCNN models to detect the unknown sketches with high accuracy, as displayed in the top row in Figure 10. Similarly, the second SketchGraph dataset highlights that a less representative dataset leads to problems for all models, as incorrect features are learned from the synthetic sketches. A visual example of the detection results with the SketchGraph 2 dataset and the YOLOv5 S model is depicted in the lower right corner of Figure 10. The influence of the models on the detection results becomes \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Unknown Assemblies**} \\ \hline
**Dataset** & \multicolumn{2}{c}{**SketchGraph 1**} & \multicolumn{2}{c}{**SketchGraph 2**} & \multicolumn{2}{c}{**SketchGraph 3**} & \multicolumn{2}{c}{**SketchGraph 4**} & \multicolumn{2}{c}{**Pillow 1**} & \multicolumn{2}{c}{**Pillow 2**} \\ \hline
**Metric** & **mAP50** & **mAP** & **mAP50** & **mAP** & **mAP50** & **mAP** & **mAP50** & **mAP** & **mAP50** & **mAP** & **mAP50** & **mAP** \\ \hline YOLOv5 S & 0.659 & 0.419 & 0.274 & 0.180 & **0.904** & **0.554** & 0.893 & 0.537 & 0.492 & 0.170 & 0.559 & 0.239 \\ YOLOv5 M & 0.717 & 0.432 & 0.193 & 0.149 & **0.904** & **0.567** & 0.868 & 0.537 & 0.485 & 0.188 & 0.561 & 0.248 \\ YOLOv5 L & 0.688 & 0.450 & 0.200 & 0.142 & **0.906** & 0.562 & 0.891 & **0.565** & 0.597 & 0.226 & 0.723 & 0.283 \\ MASK RCNN r50 & 0.111 & 0.050 & 0.080 & 0.030 & **0.501** & **0.184** & 0.451 & 0.131 & 0.189 & 0.124 & 0.011 & 0.007 \\ MASK RCNN r101 & 0.110 & 0.037 & 0.140 & 0.055 & **0.642** & **0.238** & 0.575 & 0.195 & 0.142 & 0.062 & 0.061 & 0.044 \\ Faster RCNN r50 & 0.352 & 0.223 & 0.365 & 0.241 & 0.628 & 0.376 & **0.664** & **0.430** & 0.144 & 0.026 & 0.392 & 0.217 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation metrics for all models and training datasets on unknown data with best results per model highlighted. Figure 9: Example sketches for different assembly models from the second unknown test dataset  
apparent in the Pillow datasets, where the one-stage methods achieved better results than the two-stage ones. A closer look at the RCNN models indicates that the results vary greatly and, thus, no model can be clearly identified as the best for the given task. In contrast, the YOLO models show that the M and L versions are best suited for recognition, as they also delivered higher detection accuracies for the Pillow dataset than the S version. The structure of the test dataset also influences the evaluation, particularly since the focus was more on realistic drawings and less on quantity. Consequently, symbols were included in the training dataset, but were not present in the test dataset, e.g., the ball joint. However, this problem was tolerated, because the goal of the recognition model was to be as broadly applicable as possible, rather than being a specific solution for only three or four symbols. This particular requirement also leads to detection problems. In this study, the confusion between swivi joints and general bearings occurred frequently, which is also visible in the lower left result image in Figure 10. Since the bearing symbol virtually consists of two swivi symbols, the confusion is quite understandable.