# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 6 Conclusions  
In this study, we proposed a UAV semantic segmentation dataset specifically designed for UAV segmentation tasks. The dataset is constructed by integrating three publicly available UAV detection and tracking datasets. We presented an automated method to convert object detection bounding box annotations into semantic segmentation masks. This method effectively generates accurate masks and significantly reduces the time required compared to manual annotation. The resulting dataset is large-scale, consisting of up to 605,045 images across training, validation, and testing splits. In addition, we introduced a novel lightweight model, named ThinDyUNet, designed for real-time semantic segmentation. Experimental results show that the proposed model has a significantly smaller size compared to state-of-the-art models, yet achieves competitive mIoU performance. Qualitative analysis further demonstrates that the proposed model is capable of accurately generating segmentation masks for both RGB and IR image inputs. Despite its compact architecture, ThinDyUNet outperforms or matches the performance of larger, more complex models, highlighting its efficiency and effectiveness. For future work, we aim to extend the dataset to support instance and panoptic segmentation tasks to further advance research in UAV localization. Moreover, we plan to conduct additional experiments by deploying the proposed model on an embedded system mounted on a drone to evaluate its real-world performance and real-time processing capabilities.  
Conceptualization, S.-C.K.; methodology, S.-C.K. and Y.M.J.; software, S.-C.K.; validation, S.-C.K. and Y.M.J.; formal analysis, S.-C.K.; investigation, S.-C.K.; resources, S.-C.K. and Y.M.J.; data curation, S.-C.K.; writing--original draft preparation, S.-C.K.; writing--review and editing, S.-C.K.; visualization, S.-C.K.; supervision, Y.M.J.; project administration, Y.M.J.; funding acquisition, S.-C.K. and Y.M.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Research Institute for defense Technology Planning and advancement (KRIT) grant funded by the Korea government (DAPA (Defense Acquisition Program Administration)) (KRIT-CT-23-041, LiDAR/RADAR Supported Edge AI-based Highly Reliable IR/UV FSO/CCC Specialized Research Laboratory, 2024).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** &
\begin{tabular}{c} **Model** \\ **Parameters (M)** \\ \end{tabular} & **Val Precision** & **Val Recall** & **Val Dice** & **Val mIoU** \\ \hline ThinDyUNet (with dynamic convolution) & 1.3 & 0.973 & 0.754 & 0.725 & 0.647 \\ ThinDyUNet (with regular convolution) & 15.3 & 0.953 & 0.786 & 0.819 & 0.755 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model ablation study on validation set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** &
\begin{tabular}{c} **Model** \\ **Parameters (M)** \\ \end{tabular} & **Test Precision** & **Test Recall** & **Test Dice** & **Test mIoU** \\ \hline ThinDyUNet (with dynamic convolution) & 1.3 & 0.872 & 0.75 & 0.744 & 0.646 \\ ThinDyUNet (with regular convolution) & 15.3 & 0.953 & 0.444 & 0.475 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model ablation study on test set.  
Data Availability Statement: The dataset proposed and utilized in this work can be publicly accessed at [[https://github.com/SCKIMOSU/uav](https://github.com/SCKIMOSU/uav)]([https://github.com/SCKIMOSU/uav](https://github.com/SCKIMOSU/uav)) (accessed on 17 June 2025).