# Object-Environment Fusion of Visual System for Automatic Pear Picking
## 4 Discussion  
In the experiments on instance segmentation, Mask R-CNN(Swin Tiny) outperformed YOLOv5, YOLOv8, and Mask R-CNN(ResNet-50). In the S1 dataset, the AP(Bbox) of Mask R-CNN(Swin Tiny) had a 0.02, 0.07, and 0.08 improvement over that of YOLOv5, YOLOv8, and Mask R-CNN(ResNet-50). The AP(Mask) of Mask R-CNN(Swin Tiny) had a 0.09, 0.01, and 0.11 improvement over that of YOLOv5, YOLOv8, and Mask R-CNN(ResNet-50). In the S2 dataset, the AP(Bbox) of Mask R-CNN(Swin Tiny) showed an improvement of 0.06, 0.05, and 0.14 over YOLOv5, YOLOv8, and Mask R-CNN(ResNet-50). The AP(Mask) of Mask R-CNN(Swin Tiny) showed an improvement of 0.02, 0.03, and 0.23 over YOLOv5, YOLOv8, and Mask R-CNN(ResNet-50). However, Mask R-CNN(Swin Tiny) was time-consuming. Therefore, Mask R-CNN(Swin Tiny) is not optimal for applications with limited hardware resources and stringent real-time requirements. In this research, YOLOv5 had the smallest number of parameters and it was better than the latest YOLOv8. Meanwhile, the performance difference between YOLOv5 and Mask R-CNN(Swin Tiny) was not very significant. It can be seen that YOLOv5 maintained medium accuracy while meeting the real-time requirements. In addition, it was found that the worst identification occurred in Mask R-CNN(Swin Tiny), YOLOv5, YOLOv8, and Mask R-CNN(ResNet-50) under the condition of insufficient illumination. Therefore, future research should focus on refining the model and enhancing its performance under complex illumination.  
In the experiments on picking pose estimation, the proposed method had a significant improvement over Frustum-B and VPM. In the S1 dataset, the SR of the proposed method outperformed that of Frustum-B by 2.28%, 2.01%, and 1.67% within the ranges of 30\(\sim\)50 cm, 50\(\sim\)90 cm, and 90\(\sim\)120 cm. The SR of the proposed method outperformed that of VPM by 4.80%, 3.81%, and 2.78% within the ranges of 30\(\sim\)50 cm, 50\(\sim\)90 cm, and 90\(\sim\)120 cm. In the S2 dataset, the SR of our method had a 9.34%, 11.77%, and 5.60% improvement over that of Frustum-B within the ranges of 30\(\sim\)50 cm, 50\(\sim\)90 cm, and 90\(\sim\)120 cm. The SR of the proposed method had a 10.51%, 12.16%, and 6.93% improvement over that of VPM within the ranges of 30\(\sim\)50 cm, 50\(\sim\)90 cm, and 90\(\sim\)120 cm. The proposed method provided multiple collision-free picking poses, while Frustum-B and VPM only provided unique solutions. Therefore, the proposed method shows better adaptability in the field. Additionally, the picking pose generated by our method featured a straightforward rotation, which typically involved only one rotation around one coordinate axis. In contrast, the picking poses proposed by Frustum-B and VPM entailed more complex rotations. When these picking poses were converted to the coordinate frame of the robot arm, there was a likelihood of obtaining an impossible inverse solution. This would result in the failure of robot picking. Moreover, it was found that the distance between the object and camera had an impact on the performance. The experimental results indicated that the range of 50\(\sim\)90 cm was the optimal distance. This is attributed to the fact that the depth data were the most stable within the range of 50\(\sim\)90 cm.  
We analyzed some failure cases, which revealed two aspects that affect the performance of picking pose estimation. Firstly, during instance segmentation, it is possible to mistakenly identify the background as part of the object. This phenomenon would cause an increase in errors in picking position. Secondly, the depth camera was susceptible to variations in illumination, which would result in the loss of depth data. This deficiency made it challenging to accurately perceive obstacles. As a result, some of the estimated candidates could actually collide with the obstacles. Therefore, in practical applications, the depth camera is also one of the limitations that must be addressed.