# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 3 Method
### Network Architecture
#### 3.2.2 Grasping Region Feature Enhancement Fusion Module  
In the feature fusion stage, in order to further enhance the expression ability of the grasping region and solve the problem of non-consistency between the grasping region and the full view of the object, a grasping region feature enhancement fusion (GREF) module is proposed. As shown in Figure 5, this module consists of two ordinary convolutions, one omni-dimensional dynamic convolution and the SA module, which enhances the expression ability of the grasping region features through this two-branch feature fusion.  
On the one hand, the deep feature X is sequentially subjected to ordinary 3 \(\times\) 3 convolution, 3 \(\times\) 3 omni-dimensional dynamic convolution, and ordinary 3 \(\times\) 3 convolution to obtain the feature F\({}_{2}\); on the other hand, the feature X is passed through the SA module to obtain the feature F\({}_{1}\); finally, the feature F\({}_{1}\) and F\({}_{2}\) are directly added and the feature Y is output through the ReLu activation function.  
Figure 4: The structure of omni-dimensional dynamic convolution.  
Figure 5: The structure of GRFF module.  
Figure 3: Illustration of Hadamard product operations for the four attention weights with the convolution kernel in ODConv. (**a**) Hadamard product along the spatial dimension; (**b**) Hadamard product along the input channel dimension; (**c**) Hadamard product along the output channel dimension; (**d**) Hadamard product along the convolution kernel dimension.  
The design of Shuffle Attention (SA) incorporates the ideas of group convolution, spatial attention mechanisms, channel attention mechanisms, and ShuffleNetV2 [33]. As shown in Figure 6, in the SA module, the input feature map \(X\in\mathbb{R}^{c\times h\times w}\) is first divided into g groups along the channel dimension, denoted as \(X=\left[X_{1},\cdots,X_{g}\right],\ X_{k}\in\mathbb{R}^{c/g\times h\times w}\); this part corresponds to the group part of Figure 6. Then, it is further divided into two sub-features along the channel dimension, denoted as \(X_{k1},X_{k2}\in\mathbb{R}^{c/2g\times h\times w}\). Sub-feature \(X_{k1}\) passes through the channel attention branch to obtain the dependencies between different channels. Sub-feature \(X_{k2}\) passes through the spatial attention branch to obtain the spatial dependencies between features. The channel attention branch first performs global averaging pooling (GAP) to obtain the feature vectors \(s\in\mathbb{R}^{c/2g\times 1\times 1}\):  
\[s=\mathcal{F}_{gp}(X_{k1})=\frac{1}{h\times w}\sum_{i=1}^{h}\sum_{j=1}^{w}X_{k 1}(i,j) \tag{6}\]  
and then obtains the channel attention weights through the Sigmoid activation function after linear transformation:  
\[X_{k1}^{\prime}=\sigma(\mathcal{F}_{c}(s))\cdot X_{k1}=\sigma(W_{1}s+b_{1}) \cdot X_{k1} \tag{7}\]  
where \(W_{1},b_{1}\in\mathbb{R}^{c/2g\times 1\times 1}\).  
The spatial attention branch will first perform Group Norm (GN), and then, after linear transformation, the spatial attention weights are obtained by Sigmoid activation function, expressed as follows:  
\[X_{k2}^{\prime}=\sigma(W_{2}\cdot GN(X_{k2})+b_{2})\cdot X_{k2} \tag{8}\]  
where \(W_{2},b_{2}\in\mathbb{R}^{c/2g\times 1\times 1}\).  
After that, the results of the two types of attention are concatenated in the channel dimension., i.e., \(X_{k}^{\prime}=\left[X_{k1}^{\prime},X_{k2}^{\prime}\right]\in\mathbb{R}^{c/g \times h\times w}\). Finally, the channel shuffle operation is used to ensure the interaction between the sub-features of each group.