# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation
### Experimental Results and Analysis
#### 4.4.1 Comparison Experiments on Cornell Dataset  
To facilitate a comprehensive comparison of the performance of our model with those in related studies, the Cornell dataset was split using the image-wise split (IW) and object-wise split (OW) methods [8; 9]:  
1. IW: All images in the Cornell dataset are randomly divided into a training set and a test set. The objects in the test set may have been seen in the training set, but with different positions and orientations. The purpose of splitting by image is to test the network's grasp detection ability for the same object but at different placement positions. 2. OW: All the images in the Cornell dataset are divided into training and test sets by object category, and the images of the objects that have been used in the training set are not used again in the test set. Its purpose is to test the network's grasp detection ability for the unknown objects. Shown in Table 1 is a comparison of the detection results among the different methods using the Cornell dataset with the IW and OW splitting modes. From Table 1, it can be seen that compared with some existing methods, the ODGNet not only achieves the best detection accuracy of 98.4% in the IW splitting mode, but also achieves the best performance of 97.8% in the OW splitting mode, which indicates that the detection of unknown objects is extremely effective. Moreover, the detection speed of a single image is 21 ms, which fully satisfies the requirement of real-time grasp detection. In order to gain a more intuitive understanding of the detection effect of ODGNet on the Cornell dataset, this paper chooses the GR-ConvNet algorithm, for which a very good grasp detection effect has been reported in recent years, for comparison. In Figure 9, the grasp detection results of the ODGNet algorithm and GR-ConvNet algorithm on the Cornell dataset are shown. In Figure 9, the first object detected is a red hat. From the grasping quality map and grasping width map generated by GR-ConvNet, its predicted grasping region is distributed along the edge of the hat, and the overall trend is correct. However, its score for the whole grasping region is very low, and it cannot obtain good grasp detection results. In contrast, the ODGNet algorithm predicts the grasp quality map with a score close to 1 for the edge of the hat, which predicts the grasping region very well. The second object is a bowl, whose graspable region should be the edge of the bowl, and the interior of the bowl is not graspable. However, the GR-ConvNet algorithm generates a grasp quality map that treats all regions of the bowl as graspable regions, which in turn generates grasp rectangles that are also located in the interior of the bowl. The ODGNet algorithm, on the other hand, accurately predicts the edge grasping regions. For these two objects, they clearly exhibit the characteristic of inconsistency between the graspable region and the full view of the object. The GR-ConvNet algorithm only extracts the features by ordinary convolution and does not use the attention mechanism, so it has an insufficient learning ability for the relationship between the graspable region and the full view of the object. On the contrary, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Accuracy (\%)**} & \multirow{2}{*}{**Time (ms)**} \\ \cline{3-3} \cline{5-5}  & & **IW** & & & \\ \hline Lenz [4] & SAE & 73.9 & 75.6 & 1350 \\ [PERSON] [6] & ResNet-50 & 89.2 & 88.9 & 103 \\ [PERSON] [8] & GG-CNN & 73.0 & 69.0 & **19** \\ [PERSON] [37] & GraspNet & 90.2 & 90.6 & 24 \\ [PERSON] [20] & GR-ConvNet & 97.7 & 96.6 & 20 \\ [PERSON] [21] & SF-ResNetNet & 98.2 & 97.1 & 25 \\ [PERSON] [23] & TF-Grasp & 98.0 & 96.7 & 42 \\ [PERSON] & ODGNet & **98.4** & **97.8** & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of results on the Cornell dataset.