## 3 Experimental Results
### Comparative Evaluation of Detection Performance  
The performance of the proposed model was evaluated through comparisons with several models based on YOLO-v8, which shares a similar structure. The training parameters for YOLO-v8 were set as follows: the initial learning rate was 0.01 and gradually decreased over time. The momentum was set to 0.9, and the IoU threshold for NMS was set to 0.7. The models from the following studies were included for comparison: [PERSON] et al. [30], who used only YOLO-v8 in object detection; [PERSON] et al. [13], who proposed a method for detecting drones by segmenting images and enlarging object sizes; [PERSON] et al. [31], who applied a bilateral filter to segment images and suppress noise around objects, thereby enhancing detection performance; [PERSON] et al. [16], who improved detection performance by segmenting images and applying SRCNN for small object detection. Figure 7 compares the detection performance of NSSRD and the other models based on the drone flight altitude. Figure 7a shows that NSSRD outperformed the other similar models across all altitudes in terms of the drone detection rate. Notably, at an altitude of 40 m, NSSRD recorded at least a 20% higher detection rate than the other models. Figure 7b shows that NSSRD achieved a lower false detection rate than the other models at all altitudes. Notably, at an altitude of 40 m, NSSRD demonstrated superior performance with a reduction of more than 20% compared to the model of [PERSON] et al. [31]. Figure 7c shows the recall values of the model, indicating the ratio of correctly detected drones to all detected objects. NSSRD exhibited higher recall values at all altitudes, confirming that it effectively detects drones. The model of [PERSON] et al. [31] exhibited performance similar to that of NSSRD at an altitude of 10 m; however, as the altitude increased, the performance gap widened, with a difference of more than 20% at 40 m. The models of [PERSON] et al. [13] and [PERSON] et al. [16], which use image segmentation for precise detection, demonstrated a recall difference 10-20% lower than that of NSSRD, indicating vulnerability in detecting small drones. This is likely because these models are specialized for environments where the sensor-to-object distance is short or where objects are less affected by noise, such as on thehorizon of the sea. However, this study focused on drone detection in flight environments with noise, such as clouds; thus, the existing models may have led to false alarms and missed detections in such environments. Figure 8 shows the detection results of NSSRD and existing models at a drone altitude and distance of 20 and 10 m, respectively. The top-left corner of each subfigure presents a magnified view of the flying drone, and the top-right corner visualizes the noise around the drone using a Laplacian filter. In these images, the white bounding boxes represent the ground truth, the green boxes indicate correctly detected drones, and the red boxes denote false alarms. As shown in Figure 8a, the model of [PERSON] et al. [30] successfully detected the drone because the relatively short distance preserved the drone features and boundaries. However, as shown in Figure 8b,c, the models of [PERSON] et al. [13] and [PERSON] et al. [31], which rely on image segmentation followed by YOLO-based detection, failed to detect the drone. The segmentation process of these models introduced distortions when resizing the segmented images to fit the input dimensions of YOLO, thereby leading to the loss of critical object information. Similarly, [PERSON] et al. [16] attempted to minimize information loss by enhancing the image resolution using SRCNN after segmentation. However, as shown in Figure 8d, this approach inadvertently restored the noise around the object, resulting in detection failure. In contrast, NSSRD demonstrated superior performance, as shown in Figure 8e. By  
Figure 7: Drone detection performance at different flight altitudes; (**a**) TP; (**b**) FN; (**c**) recall [13; 16; 30; 31].