## 3 Results  
### Handle Detection  
Example results of handle detection are presented in Figure 4a. Four architectures for handle detection were tested. In Table 4, we show a numerical comparison of these models. They include a comparison of detector performance, calculated as mAP for Intersection over Union (_IoU_) ratios greater than 0.5, greater than 0.75, and a comparison of the average inference time of a single image. The inference was performed on a machine equipped with a quad-core processor AMD Ryzen 3 3100 supporting eight threads. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **mAP@IoU = 0.50** & **mAP@IoU = 0.75** &
\begin{tabular}{c} **Average Inference** \\ **Time [s]** \\ \end{tabular} \\ \hline SSD Inception V2 & 0.419 & 0.101 & **0.042** \\ EfficientDet D0 & 0.727 & 0.212 & 0.114 \\
**CenterNet ResNet50 V1** & 0.928 & **0.473** & 0.253 \\ CenterNet ResNet101 V1 & **0.932** & 0.461 & 0.405 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between methods for handle detection. The best results are bolded. The shortest single-image inference time was obtained by the network with SSD Inception V2 architecture. At the same time, this network had the lowest mAP score for both IoU thresholds. The EfficientDet D0 network achieved better results of 73.5% and 109.9%, respectively, relative to the SSD Inception V2 network, but the cost was the inference time, which increased by 169.8% compared to the SSD Inception V2 network. The best mAP value for the threshold set to 0.5 was obtained by the CenterNet network with ResNet101 as the encoder. This network, however, had the longest inference time of all the networks tested. The mAP value, for the threshold set to 0.5, was 0.43% worse for the same network with ResNet50 acting as an encoder, with its inference time being 37.49% shorter compared to the network with ResNet101. In addition, the network with ResNet50 achieved a better mAP score for the IoU 0.75 threshold by 2.60%. This means that the network matched the positions of the surrounding rectangle slightly better. Relative to the EfficientDet D0 network, CenterNet ResNet50 achieved better mAP scores by 27.65% and 123.11%, but the processing time for a single image was 121.24% higher. From the results presented in Table 4, we conclude that the EfficientDet D0 network has a problem locating the surrounding rectangle accurately, as the mAP@IoU = 0.75 value is 70.84% worse than the mAP@IoU = 0.5 value. In Figure 5, we show a comparison of EfficientDet D0 and CenterNet ResNet50 network detections on selected images from the test set. In the figures, we show the differences in the performance of these two networks. In Table 4, the EfficientDet D0 network has a lower recall than the CenterNet network, which is manifested by the number of detected handles in Figure 5a,b. In Figure 5, we show a difference in the positioning accuracy of the rectangle surrounding the handle. The EfficientDet D0 network has trouble capturing the entire object, and sometimes the object bounding box is too large. Both networks are capable of generating false-positive predictions, but the CenterNet network gives them lower confidence. By setting a higher detection threshold, such predictions can be discarded while maintaining a large number of correct detections. Figure 4: Example results of front handle detection (**a**) and surface segmentation (**b**). On the **left** is presented the object on which the estimation was performed. Ground truth is presented in the **center**, and prediction is on the **right**. Handle detection plays a very important role in the environment model building system. In addition to influencing the position of the handle in 3D space, it also affects the  
Figure 5: Qualitative comparison between results obtained from the EfficientNet D0 (**a**) and CenterNet with ResNet50 encoder (**b**). accurate estimation of the axis of rotation, because one of the inputs of the axis estimation module is the mask of detected handles, which is then fed to the neural network.