# Grasp Detection Combining Self-Attention with CNN in Complex Scenes
## 5 Experiments
### Grasp Detection in Complex Backgrounds  
Our main objective was to enable the application of the grasp detection model in complex backgrounds. To evaluate the capabilities of our model in these scenarios, we captured real-life images with objects placed in complex backgrounds and assessed its performance using the model trained on the Cornell dataset, as shown in Figure 11. To help better understand how the proposed model makes grasping judgments about objects, we visualized the heatmap of the quality feature map, as show in the second and fourth rows.  
The validity of the FFB: Is the proposed structure, which comprises transformer blocks and the bridge structure, effective for identifying prediction? To address this question, we visualized the output feature maps of the proposed structure to examine their impact during the prediction process. The visualizations are shown in the fifth row. It is apparent that the proposed structure's output demonstrates a high degree of similarity to the quality feature map, indicating a significant impact on the final detection results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**P/H/M/L** & **Without 2D Gaussian Kernel** & **With 2D Gaussian Kernel** \\ \hline
7/256/512/3 & 93.2\% & 94.3\% \\
7/256/512/6 & 96.6\% & 97.7\% \\
7/256/512/9 & 95.5\% & 95.5\% \\
16/1024/2048/6 & 96.6\% & 96.6\% \\
7/1024/2048/6 & 97.7\% & 97.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of different configurations on the Cornell dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**P/H/M/L** & **Without 2D Gaussian Kernel** & **With 2D Gaussian Kernel** \\ \hline
7/512/1024/9 & 69.3\% & 70.2\% \\ \hline
7/1024/2048/6 & 71.7\% & 72.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The accuracy of different configurations on VMRD.  
Furthermore, we tested the grasp detection performance of the proposed method in more complex backgrounds. These scenes contained wide ranges of irregular patterns, or orderly arranged color patterns. These patterns are difficult to be mitigated by traditional image processing methods and cause strong interference for detection. The results are as shown in Figure 12.  
For Scenes II and III, our model could easily detect suitable grasps. For Scenes I and IV, the more complex patterns in the background became an obstacle, and our model only responded positively to part of the objects. Even though it was trained on the simple single-object Cornell dataset, it showed advantages compared to the grasping CNN.  
The experiments showed that our method can model the relationships between objects and the entire scene, as it induced and expressed the visual relationships between object  
Figure 11: Examples of grasp detection by grasping CNN [7] and our method. The first and second rows show the results for CNN. The third, fourth and fifth rows show the results for our method.  
Figure 10: The accuracy of different models on multi-object VMRD.  
features and background configurations. These designed structures positively impacted the grasping prediction and visually exhibited a segmentation-like effect.