# Accurate Instance-Based Segmentation for Boundary Detection in Robot Grasping Application
## 4 Experimental Results and Analysis
### Result and Evaluation  
The following Figure 7 illustrates the comparison between the results of the original Mask R-CNN and after applying our approach. Our goal could be observed that it reaches segmentation accuracy better than the original framework. The object mask generated by  
Figure 6: Training loss original network with Resnet 101 and Resnet 50. Mask R-CNN could not deliver enough information about the object while our approach covers most of the entire object even though the objects were obscurely located. The border regions or even the regions that have less brightness are usually challenging for 2D algorithms to approach, but all of them are kept in our research as below. The increased accuracy of covering all regions of the object will submit a prosperous pose estimation of objects to determine the grasping position and grasping orientation for the robot arm. As shown in Figure 7, our approach could both cover and segment the edge region. The result shows that it classified the edge regions and added these regions to the corresponding mask generated by Mask R-CNN. All regions of the objects are obtained completely and then can approve the grasping plan. The poor masks usually happen when the objects are located close together. Thus, to prove the performance of our approach, we focus on 50 experiments with overlapping located objects as shown in Figure 7a. Figure 7b shows that the original masks generated by Mask R-CNN are poor; this means these masks could not contain all of the information of the objects. This overlooked information is separated after the subtraction step as shown in Figure 7c. As described above, for segmenting this information, we continuously place each original mask to the subtraction result and applied Euclidean Cluster Extraction. Despite the fact that the size of the dataset is modest and the limitation mentioned before, our approach enables the hardest region to segment as shown in Figure 7c. We evaluate the success of the method by average precision (AP) and IoU (Intersection over Union) [28] indicator. The AP is evaluated at an IoU threshold of 0.5 to 0.95 with  
Figure 7: The comparison image between (**a**) original images, (**b**) Mask R-CNN and (**c**) our approach. Our approach can find all the borders of the object, as shown in the white circle. an interval of 0.05. The final mAP (mean Average Precision) result is the average of the 10 measurements. \[\text{IoU}=\frac{\text{area of overlap}}{\text{area of union}} \tag{2}\]  
True positive, false positive, true negative, and false negative are abbreviated as TP, FP, TN and FN, respectively, accuracy [28] is:  
\[\text{accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
Our method obtained not only higher IoU but also higher mAP than the original Mask RCNN result. To verify the effectiveness of our method, we practice throughout both backbone Resnet 101 and Resnet 50. This experiment will ensure that the improvements come from the difference of backbone Resnet or from our approach. The comparison is presented as Tables 1 and 2, which compare the IoU and mAP between each object cup, blue box, cylinder and white box through Mask R-CNN with backbone Resnet 101, Mask R-CNN with backbone Resnet 50, our approach applying for Mask R-CNN Resnet 101 and our approach applying for Mask R-CNN Resnet 50. When using only 3D traditional segmentation algorithms, it evidently achieves a low level of IoU. The combination improved considerably compared to initial 3D segmentation algorithms. To detail our exercise, Figure 8 illustrates the IoU of all four types of objects through Mask R-CNN and our proposed approach. The difference of the backbone network does not make a change in the result. The original network is robust in the object segmentation task but testing with only the state of the overlapping objects shows its weakness. In general, Mask R-CNN grants an unsteady result. Sometimes the IoU of each object exceeds 0.9. It happens four times with Cup, four times with Cylinder, only two times with White box and zero times with Blue box.