# An Improved Small Target Segmentation Model Based on Mask Dino
## 4 Experiment and Result Analysis
### Ablation Study  
Table 4 reveals that the enhanced FPN module in the semantic segmentation task enables the model to harness both the profound semantic insights from deeper layers and the fine-grained details from shallower layers. Such a fusion is especially vital for the detection of small objects, as they are more distinct in the lower-level features but can become obscured at higher levels. Consequently, this advancement has led to a 0.96% increase in mIoU over the baseline model, underscoring the significance of this architectural enhancement. To intuitively perceive the segmentation effect, we have visualized the results as shown in Figure 4. As demonstrated in Table 5, the FFMMask Dino model shows superior performance in the task of instance segmentation on the COCO dataset. With the improved Swin Transformer and the proposed FFPN module, the model enhances its capability to process small-sized objects that are often overlooked by traditional methods. In the model based on the ResNet-50 backbone network, the FFMMask Dino model achieved a 0.1 increase in Mask AP and a 0.7 increase in Box AP over the original Mask Dino model. The FFPN \begin{table}
\begin{tabular}{c c c} \hline \hline
**Backbone Network** & **FFPN** & **mIoU/\%** \\ \hline ResNet-50 backbone & & 41.19 \\ ResNet-50 backbone & \(\surd\) & 42.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation Experiment Results of Semantic Segmentation on the ADE20K Dataset. module, through its improved connections and feature fusion strategies, ensures that high-resolution detail information is better retained and utilized within the model, enhancing the recognition ability for small objects. Compared to the original model, Mask AP improved by 0.4, Box AP by 0.9, and \(AP_{50}^{mask}\), \(AP_{75}^{mask}\), \(AP_{5}^{mask}\), and \(AP_{L}^{mask}\) metrics increased by 0.5, 0.6, 0.1, and 0.2, respectively. In the model with ResNet-50 as the backbone, the FFPN module assists the model in effectively capturing hierarchical details of images when processing local features and texture information, leading to a 1 increase in Mask AP and a 1.3 increase in Box AP for the FFMask Dino model over the original model. With each additional improved module, the overall performance of the model increases, and the final model that incorporates all the improved modules performs the best. As indicated in Table 6, the improved Swin Transformer and the proposed FFPN module both contribute to enhancing the model's performance in the task of panoptic segmentation. In the model employing the Swin Transformer as the backbone, the refined  
Figure 4: (**a**) Original images from the ADE20K dataset; (**b**) visualization of the label map; (**c**) visualization of the segmentation results from Mask Dino; (**d**) visualization of the segmentation results from FFMask Dino in this paper. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Backbone Network** & **FFFN** & **Transformer *** & **Mask AP** & **Box AP** & \(\mathbf{AP_{50}^{mask}}\) & \(\mathbf{AP_{75}^{mask}}\) & \(\mathbf{AP_{7}^{mask}}\) & \(\mathbf{AP_{M}^{mask}}\) & \(\mathbf{AP_{L}^{mask}}\) \\ \hline ResNet-50 backbone & & & 39 & 42.8 & 59.5 & 42.1 & 18.4 & 42.1 & 58.4 \\ ResNet-50 backbone & \(\surd\) & & 40 & 44.1 & 60.5 & 43.1 & 19.1 & 42.7 & 59.9 \\ \hline Swin backbone & & & & 46.6 & 51.7 & 70.3 & 50.3 & 25.5 & 50.3 & 68.9 \\ Swin backbone & \(\surd\) & & 47 & 52.6 & 70.8 & 50.9 & 25.6 & 50.3 & 69.1 \\ Swin backbone & & \(\surd\) & 46.7 & 52.4 & 70.3 & 50.3 & 24.6 & 50.3 & 68.9 \\ Swin backbone & \(\surd\) & & \(\surd\) & 47.1 & 52.6 & 70.9 & 50.9 & 25 & 50.4 & 69.5 \\ \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 5: Ablation experiment results of instance segmentation on the COCO Dataset.