## 7 Discussion  
We conducted a comprehensive experiment to evaluate the effectiveness and advancement of our grasp detection algorithm, FAGD-Net. Firstly, we achieved accuracies of 96.5% and 98.9% on Jacquard and Cornell grasp dataset, respectively, showcasing the robustness of our approach. By comparative analysis with existing algorithms, we further highlight the advantages of FAGD-Net.  
Subsequently, we conducted ablation studies to validate the contributions of the proposed Res-EMA module and FFPM. Additionally, we introduced Res-SE and Res-CoA modules based on Squeeze-and-Excitation (SE) and Coordinate Attention (CoA) mechanisms from previous grasp works [13; 14]. Through comparisons with Res-EMA and Res-block, we emphasized the effectiveness of the Res-EMA module. Furthermore, we compared feature concatenation and fusion methods within the model to highlight the advantages of our FFPM.  
Figure 17: Robot grasping in different cluttered scenes.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Method**} & **Objects in Cluttered Scenarios** \\ \cline{2-3}  & **Accuracy (\%)** \\ \hline [PERSON] et al. [4] & 87 \\ [PERSON] et al. [8] & 84 \\ [PERSON] et al. [19] & 90.2 \\ [PERSON] et al. [40] & 90 \\ [PERSON] et al. [41] & 90 \\ [PERSON] et al. [42] & 87 \\ [PERSON] & 91.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results in cluttered scenarios.  
Finally, real-world robot grasping experiments were conducted, achieving success rates of 95.0% for single-object scenes, 93.3% for multi-object scenes, and 91.0% for cluttered scenes.  
In the tests, we conducted grasping experiments using diverse objects that varied in size, orientation, and type. FAGD-Net demonstrated strong generalization capability. In some other environments, our FAGD-Net can also be applicable, such as on tabletops with textured backgrounds and in low-light environments. The main reason is that textured backgrounds and low-light conditions do not severely affect depth information acquisition.  
Although FAGD-Net demonstrated satisfactory grasping capabilities, some potential issues were identified. Occasional collisions with other objects occur during the grasping process, leading to the accidental removal of the object from the scene. Additionally, instances of grasping multiple objects simultaneously were observed, as depicted in Figure 18. There are two main types of failed grasping as follows: (1) when the robot approaches the object to be grasped, the gripper is blocked by cluttered objects and does not have enough space for the parallel-jaw gripper. (2) The grasped target slips from the parallel-jaw gripper because the center of gravity is unstable. Further improvements and optimizations are required to address these challenges and enhance the overall performance of the algorithm.  
It is worth noting that we consider the grasp configuration that infers the highest confidence from the image. The attributes of the target, such as a certain object's density and rigitiity, are not accounted for in the model. The current focus of this study is primarily on objects grasping and placing. We will further study and improve upon them in the future.  
Additionally, in this study, the grasped objects have different shapes, making them more suitable for grasp configurations using parallel grippers. In some scenarios, using vacuum/magnetic grippers may be more efficient, such as for flat surfaces or the objects are easily deformed.