# An Improved Small Target Segmentation Model Based on Mask Dino
## 5 Conclusions  
To address the issue of inaccurate segmentation of small-sized objects in semantic segmentation, instance segmentation, and panoptic segmentation tasks, where the target information is progressively weakened during the feature extraction process of the Mask Dino model, we propose a segmentation framework called FFMask Dino based on Mask Dino. Firstly, we introduce the scaled cosine attention and Log-cpb methods in the main backbone network, the Swin Transformer. By calculating the cosine similarity between features, these methods can better capture subtle changes in small objects. This approach is more sensitive to the detailed features of small-sized objects, thereby enhancing the model's ability to identify and locate small targets. It also increases the importance of small object categories, making the model pay more attention to these targets during training. Additionally, by adjusting the cumulative probability distribution of model predictions, we increase the weight of small objects in the probability distribution, mitigating the adverse effects of their scarcity on recognition. Secondly, by modifying the network structure, we stabilize the training process, improve learning dynamics, and enhance the model's generalization ability to new data. This change in information flow dynamics allows each layer to more freely adjust its learned representations during training, without over-relying on the normalization of inputs. Lastly, we propose the FFPN module, which reduces the model's parameter count by streamlining the FPN structure while maintaining performance. This optimized FPN reduces unnecessary computations, thereby speeding up model inference. It also integrates detailed information from multi-scale features and high-level semantic information to supplement object features, thereby improving segmentation accuracy. Experimental results demonstrate that our method outperforms other mainstream methods in semantic segmentation on the ADE20K dataset and in instance segmentation and panoptic segmentation on the CoCo dataset. It can be applied as an intelligent and effective segmentation method in real-life scenarios. However, our work has limitations. We did not optimize object edges, and the segmentation texture of irregular small objects needs improvement. Future work will focus on enhancing the model's attention to irregular small objects by improving the architecture or introducing new learning mechanisms. Additionally, FFMask Dino, based on Mask Dino, has a high computational demand when processing images of different resolutions, requiring substantial computing power. This is particularly limiting in resource-constrained environments. We plan to optimize  
Figure 5: (**a**) Original Image; (**b**) visualization of the label map; (**c**) visualization of the segmentation results from Mask Dino; (**d**) Visualization of the segmentation results with the Transformer \({}^{\star}\) module; (**e**) visualization of the segmentation results with the FFPN module; (**f**) visualization of the segmentation results from the FFMask Dino model in this paper. FFMask Dino using model compression and pruning techniques to reduce its computational load while maintaining performance. We also aim to create datasets for satellite imagery, agriculture, and forestry to apply the FFMMask Dino model in real-world applications. Conceptualization: X.C. and J.Y. ; methodology: X.C. ; software: X.C. ; validation: X.C. ; formal analysis: X.C. ; investigation: X.C. ; resources: Y.G. ; data curation: G.G. ; writing--original draft preparation: X.C. ; writing--review and editing: X.C. ; visualization: X.C. ; supervision: [PERSON]; project administration: X.C. ; funding acquisition: J.Y. ; Equipment and Debugging: Y.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Grant No. 62366019 for the project \"Research on Feature Selection and Classification Methods for Multi-label Data\".