## 6 Results and Analysis  
### Grasp Detection in the Jacquard Dataset  
In this task, the Jacquard was used, with 90% of the images used for training and the remaining 10% for grasping testing. We resized the images to 240 \(\times\) 240, and during network training, the batch size was set to 8.  
In Figure 6, we visualize the detection results on the Jacquard dataset, which include representations of robot grasping points and grasping line segments, along with a heatmap indicating the confidence of grasping for each pixel and the corresponding grasping angles and widths. In experimental testing, our model achieves an accuracy of 96.5% on the Jacquard dataset. It is obvious that the grasp configuration predicted by the model is easily grasped by the robot. Additionally, our model is lightweight with approximately 260 K parameters compared with the 1.8 million parameters of GR-CNN [6]. This is because GR-CNN repetitively stacks residual blocks in the middle of the network to enhance the model's ability to extract grasp-related features, but this approach comes at the expense of efficiency and parameter count.  
As recorded in Table 1, our method outperforms works [4; 6; 33] by 12.5%, 1.9%, and 22.3%, respectively. These methods have relatively simple network architectures and do not emphasize the extraction of grasp-related features sufficiently. For instance, GG-CNN [4] only uses ordinary convolutions for down-sampling features and then up-samples them to output grasp configurations. Furthermore, our model outperforms the latest QQGNN [22] by 1.5%. Although QQGNN employs depth-wise separable convolution to make the grasping model lightweight, this approach may limit grasp-related feature learning as it only allows each channel to interact with its corresponding convolution kernel during the depth-wise convolution stage while neglecting inter-channel correlations.  
Figure 6: Grasping detection results in the Jacquard dataset.  
Some other methods adopt attention mechanisms to efficiently extract relevant grasp features. Our model outperforms the methods in [10; 11; 12; 14], by 1.9%, 0.8%, 1.7%, and 0.3%, respectively. The work [10] uses the self-attention mechanism of transformers to obtain global features. However, this approach may inadvertently discard certain local features, thus resulting in suboptimal performance. On the other hand, the works [11; 12] employ the Squeeze-and-Excitation (SE) attention mechanism. While it is effective in isolating important channel features, it overlooks spatial information within the network and lacks the ability to handle multi-scale features. Although AAGDN [14] focuses on spatial position information, it neglects the importance of interactions among spatial positions as a whole. Moreover, the limited receptive field of \(1\times 1\) convolution kernels hinders the modeling and utilization of local cross-channel interactions and contextual information. In contrast, our network addresses these issues through the Res-EMA module, which effectively adjusts the importance of feature channels while preserving precise spatial information within the channels. As a result, our grasp detection network achieves an accuracy of 96.5% on the Jacquard Grasping Dataset.