# Accurate Instance-Based Segmentation for Boundary Detection in Robot Grasping Application
## 4 Experimental Results and Analysis
### Result and Evaluation
It generally achieves low and strong volatility and is unstable. The Cup IoU grants under 0.8 many times and even reaches 0.7. The IoU indicator of the Blue box usually is under 0.7 and sometimes under 0.6. The IoU result of the Cylinder and White box IoU are more moderate fluctuations. The IoU is mostly higher than 0.7, but the movement of each two continuous samples is a huge number. All the fluctuations are caused by the overlapping, obscurity, closed location of objects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Cup IoU** & **Blue Box IoU** & **Cylinder IoU** & **White Box IoU** \\ \hline Mask RCNN (Resnet 101) & 0.835 & 0.772 & 0.825 & 0.812 \\ \hline Mask RCNN (Resnet 50) & 0.821 & 0.768 & 0.826 & 0.792 \\ \hline DoN & 0.264 & 0.209 & 0.314 & 0.195 \\ \hline Euclidean Cluster Extraction & 0.310 & 0.287 & 0.257 & 0.358 \\ \hline Ours (Resnet 101) & 0.872 & 0.837 & 0.865 & 0.881 \\ \hline Ours (Resnet 50) & 0.870 & 0.834 & 0.859 & 0.879 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IoU of our approach in comparison to original Mask R-CNN and 3D traditional segmentation algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP** & **mAP\({}_{\text{cup}}\)** & **mAP\({}_{\text{blue box}}\)** & **mAP\({}_{\text{cylinder}}\)** & **mAP\({}_{\text{white box}}\)** \\ \hline Mask RCNN (Resnet 101) & 0.39 & 0.399 & 0.336 & 0.465 & 0.365 \\ \hline Mask RCNN (Resnet 50) & 0.38 & 0.387 & 0.345 & 0.448 & 0.365 \\ \hline Ours (Resnet 101) & 0.46 & 0.446 & 0.429 & 0.524 & 0.437 \\ \hline Ours (Resnet 50) & 0.46 & 0.456 & 0.421 & 0.534 & 0.447 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean average precision of our approach in comparison to original Mask R-CNN. Figure 8: _Cont._  
Meanwhile, after using our method for doing experiments, the results have a slight fluctuation, greater IoU and are steadier. The IoU of the Blue box is now higher and around 0.8. In all likelihood, the IoU of the Cylinder, Cup and White box are greater or approximate to 0.85. In detail, our exceptional result in obtaining higher IoU is the case for the majority. At some points, our score is less accurate than the original network, but it is close to IoU of the original network result. With regard to this issue, it is driven up by the scabrous of object contours, which come from the downsampling step in point cloud processing. As opposed to this case, when our approach achieves greater results, it reaches a more significantly advanced IoU than the original Mask R-CNN. Our proposed approach upgrades the ability of Mask R-CNN to detect and segment the edge areas more accurately and with more stability, even in the difficult cases. The more stable IoU indicator is the premise leading to the greater mAP. Table 2 denotes the effectiveness of our approach in effort to improve the accuracy of segmentation. Although all practices are implemented with severe cases to emphasize the ability of dealing with edge region. The backbone Resnet 101 and Resnet 50 does not make too much of a difference in the result throughout both the original model and our method. Our approach achieves has the exceptional consequence of improving the performance of the original Mask R-CNN. In addition, the mean average precision increased from 0.39 to 0.46.