## 4 Conclusions  
This study proposed a detection framework NSSRD to enhance the performance of long-range small drone detection while meeting the real-time airspace monitoring requirements. The proposed model introduces a noise suppression preprocessing structure based on bilateral filtering to effectively suppress the noise generated during image segmentation and enlargement, aiming to improve small drone detection performance. Additionally, an efficient detection process integrating super-resolution image enhancement was developed to preserve the critical silhouette information of the drone as much as possible, thereby improving both the reliability and efficiency of drone detection. Furthermore, by introducing an ROI exploration process based on Laplacian filtering, the model selects only images containing drones from the segmented images, reducing unnecessary computations and enhancing overall computational efficiency and detection speed. Experimental results demonstrated that NSSRD outperformed existing related models under various environmental conditions and altitudes, with a notable improvement of up to 24% in the TP values at an altitude of 40 m. Additionally, by reducing computational load and improving detection speed, the proposed model design satisfies real-time processing requirements.  
The key contribution of this study is the presentation of an integrated approach that enhances the reliability and efficiency of drone detection, thereby advancing real-time drone monitoring and detection technologies. Future research could further expand the applicability of NSSRD through the development of additional algorithms and hardware  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{Model} & \multirow{2}{*}{\begin{tabular}{c} Grid \\ Partitioning \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Preprocessing \\ Enhancement \\ \end{tabular} } & Resolution & \multirow{2}{*}{\
\begin{tabular}{c} Detection \\ FLOPs \\ \end{tabular} } \\  & &
\begin{tabular}{c} Bilateral \\ Filter \\ \end{tabular} & & & \\ \hline [PERSON] et al. [30] & & & & & \(\surd\) & 28G \\ \hline [PERSON] et al. [13] & \(\surd\) & & & & \(\surd\) & 252G \\ \hline [PERSON] et al. [31] & \(\surd\) & \(\surd\) & & \(\surd\) & 259G \\ \hline [PERSON] et al. [16] & \(\surd\) & & \(\surd\) & \(\surd\) & 307G \\ \hline NSSRD & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 34G \\ \hline \hline \end{tabular}
\end{table}
Table 1: FLOPs per model.  
optimizations aimed at improving detection performance under various environmental conditions, such as complex backgrounds, low light conditions, or adverse weather. In addition, integrating path prediction and threat assessment algorithms after drone detection can lead to the development of a more sophisticated airspace monitoring system.  
[PERSON] and [PERSON] participated in the discussion of the work described in this paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MOE) (No. 2021R1 IIA3059973) and the Soonchunhyang University Research Fund.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.  
The authors declare no conflicts of interest.