# An Improved Small Target Segmentation Model Based on Mask Dino
## 4 Experiment and Result Analysis
### Ablation Study
Swin Transformer adjusts the intensity of attention through scaling factors, allowing it to dynamically shift the model's focus according to task requirements and the size of the target, thereby effectively improving the response to small objects, with PQ, \(PQ^{Th}\), and \(PQ^{St}\) increasing by 0.31, 0.4, and 0.17, respectively, compared to the original model. The FFPN module, through its optimized pathways for feature fusion and transmission, reduces unnecessary computations, and the improved FPN ensures better retention and utilization of high-resolution detail information within the model, enhancing the recognition capability for small objects, with PQ, \(PQ^{Th}\), _and \(PQ^{St}\)_ increasing by 0.35, 0.32, and 0.43, respectively, compared to the original model. In the model with ResNet-50 as the backbone, the FFPN module streamlines the structure to reduce the number of model parameters while maintaining performance [39], and the improved FPN provides more stable performance, enhancing the model's adaptability and robustness, with PQ, \(PQ^{Th}\), and \(PQ^{St}\) increasing by 0.9, 1.21, and 1.2, respectively, compared to the original model. Moreover, the combined use of both modules results in a greater enhancement of the model's segmentation performance, regardless of the backbone network employed. To more vividly demonstrate the improved model's segmentation performance on small objects across various segmentation tasks, a visual analysis of some of the segmentation results is presented. As can be seen from the visual results in Figure 5, comparisons have been made regarding the segmentation outcomes of these algorithms for different tasks. It is evident that even in scenarios with small targets and complex background information, the proposed FFMask Dino is capable of accurately segmenting the targets. Moreover, the segmented small targets are largely intact and precise in shape. By integrating methods such as the scaled cosine attention approach, the log-cpb method, and adjustments to the network structure to enhance the Swin Transformer and the proposed FFPN module, the FFMask Dino system was constructed as a more powerful and effective small target segmentation system. The combination of these methods also helps to improve the model's generalization capabilities, allowing it to maintain high performance when faced with new scenes and challenges. In practical applications, this comprehensive model can be widely used in fields such as autonomous driving, video surveillance, and remote sensing image analysis, providing strong technical support for small target segmentation. In the COCO dataset, we visualized the improved model as shown in Figure 5. We can observe that the model with Swin Transformer as the backbone network demonstrates enhanced segmentation performance for small objects. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone Network** & **FFPN** & **Transformer \({}^{*}\)** & **PQ** & \(\mathbf{PQ^{Th}}\) & \(\mathbf{PQ^{St}}\) \\ \hline ResNet-50 backbone & & & 46.03 & 51.61 & 37.61 \\ ResNet-50 backbone & \(\surd\) & & 46.93 & 52.82 & 38.41 \\ \hline Swin backbone & & & 54.55 & 60.57 & 45.47 \\ Swin backbone & \(\surd\) & & 54.90 & 60.89 & 45.90 \\ Swin backbone & \(\surd\) & \(\surd\) & 54.86 & 60.97 & 45.64 \\ Swin backbone & \(\surd\) & \(\surd\) & 54.95 & 61.28 & 45.39 \\ \hline \hline \end{tabular} \({}^{*}\) The Swin Transformer module, improved with the referenced Scaled Cosine Attention method, log-cpb method, and network structure adjustments, is denoted by Transformer \({}^{*}\).  
\end{table} 
Table 6: Panoptic Segmentation Ablation Experiment Results on the COCO Dataset.