# An Improved Small Target Segmentation Model Based on Mask Dino
## 4 Experiment and Result Analysis
### Comparison Experiment and Results
#### 4.3.2 Comparison of Experimental Results on the CoCo Dataset  
In an effort to substantiate the superior performance of the FFMask Dino model in segmenting small objects, a series of instance segmentation experiments were performed on the COCO dataset under uniform experimental conditions, including the environment and dataset. As depicted in Table 2, the FFMask Dino model demonstrates superior \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Iters** & **Queries** & **Crop Size** & **MIoU/\%** \\ \hline ResNet-50 backbone & & & & \\ Mask2 Former & 4 & 100 & 512 & 40.50 \\ Mask Dino & 4 & 300 & 512 & 41.19 \\ FFMask Dino (ours) & 4 & 300 & 512 & 42.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Semantic Segmentation Tasks in ADE20K Dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Iters** & **Query Type** & **Mask AP** & **Box AP** & **AP\({}_{50}^{\text{mask}}\)** & **AP\({}_{78}^{\text{mask}}\)** & **AP\({}_{5}^{\text{mask}}\)** & **AP\({}_{M}^{\text{mask}}\)** & **AP\({}_{L}^{\text{mask}}\)** \\ \hline ResNet-50 backbone & & & & & & & \\ \hline Mask2 Former & 24 & 100 queries & 38.70 & - & 59.80 & 41.20 & 18.20 & 41.50 & 59.80 \\ Mask Dino & 24 & 300 queries & 39.00 & 42.80 & 59.50 & 42.10 & 18.40 & 42.10 & 58.40 \\ FFMask DinoDino (ours) & 24 & 300 queries & 40.00 & 44.10 & 60.50 & 43.10 & 19.10 & 42.70 & 59.90 \\ \hline Swin backbone & & & & & & & & \\ \hline Mask2 Former & 24 & 100 queries & 43.70 & - & 66.00 & 46.90 & 23.40 & 47.20 & 64.80 \\ Mask Dino & 24 & 300 queries & 46.60 & 51.70 & 70.30 & 50.30 & 25.50 & 50.30 & 68.90 \\ FFMask DinoDino (ours) & 24 & 300 queries & 47.10 & 52.60 & 70.90 & 50.90 & 25.0 & 50.40 & 69.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of instance segmentation tasks on the CoCo dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Iters** & **Query Type** & **PQ** & **PQ\({}^{\text{Th}}\)** & **PQ\({}^{\text{St}}\)** \\ \hline ResNet-50 backbone & & & & & \\ \hline Mask2 Former & 24 & 100 queries & 45.90 & 51.50 & 37.40 \\ Mask Dino & 24 & 300 queries & 46.03 & 51.61 & 37.61 \\ FFMask Dino (ours) & 24 & 300 queries & 46.93 & 52.82 & 38.41 \\ \hline Swin backbone & & & & & \\ \hline Mask2 Former & 24 & 100 queries & 53.66 & 59.87 & 43.93 \\ Mask Dino & 24 & 300 queries & 54.55 & 60.57 & 45.47 \\ FFMask Dino (ours) & 24 & 300 queries & 54.95 & 61.28 & 45.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Panoptic Segmentation Tasks on the COCO Dataset. comprehensive performance in instance segmentation tasks compared to other models. Utilizing the ResNet-50 backbone network, the FFMMask Dino model has achieved a notable enhancement of 1 point in Mask AP and 1.3 points in Box AP over the Mask Dino model, \(AP^{mask}_{50}\), \(AP^{mask}_{75}\), \(AP^{mask}_{8}\), \(AP^{mask}_{M}\), and \(AP^{mask}_{1}\). These data have, respectively, improved by 1, 0.7, 0.6, and 1.5. Utilizing the Swin Transformer as the backbone network, our enhanced FFMMask Dino model has demonstrated significant advancements. It has outperformed the Mask Dino model with a notable increase of 0.5 points in Mask AP and 0.9 points in Box AP, \(AP^{mask}_{50}\), \(AP^{mask}_{75}\), and \(AP^{mask}_{M}\). These data have, respectively, improved by 0.6, 0.6, 0.1, and 0.6. This paper demonstrates that the improved method performs better in instance segmentation tasks, benefiting the segmentation of small objects in images. Table 3 clearly illustrates the FFMMask Dino model's exceptional performance in the panoptic segmentation task on the COCO dataset, outperforming other models. Utilizing the ResNet-50 backbone network, the FFMMask Dino model has seen a notable increase of 0.9 in PQ, 1.21 in metrics, and 0.8 in metrics when compared to the Mask Dino model. Furthermore, with the Swin Transformer backbone network, the FFMMask Dino model has demonstrated an enhancement of 0.4 in PQ and 0.71 in metrics against the Mask Dino model.