## 4 Experiments and Analysis
### Comparison with Other Methods
#### 4.3.2 Visual Results  
Our presentation includes a variety of application situations featuring variations in UAV target scales, intense clutter, and partial occlusion, accompanied by visual outcomes from diverse techniques, performed on the IRSTD-1k and IRMT-UAV datasets, as depicted in Figure 7. GT indicates the annotated true position of the drone in the captured images, with the top-right corner showing a detailed local view of the drone target. It can be observed that the scale of the drone changes significantly with variations in imaging distance. We collected drone images against various backgrounds, such as clouds, buildings, rural vegetation, and urban backgrounds. In these settings, the wide range of grayscale variations in the images resulted in strong background clutter and very weak visual features of the drone targets, further increasing the difficulty for the detection algorithm. Circles of varied hues are used to emphasize accurately identified targets, incorrect alerts, and undetected targets. Orange circles represent correctly detected targets, purple circles indicate false alarms, and blue circles denote missed detections. Initially, we introduced a juxtaposition of our suggested algorithm against alternative techniques amidst intense clutter environments. For medium-sized targets with scales of tens of pixels, all algorithms successfully detected the targets, although a few algorithms exhibited false alarms. With the escalation of the clutter backdrop and the diminishing scale of the target, the target becomes more obscured in the background, leading to other algorithms' inability to accurately identify the target. Conversely, the algorithm we suggest, utilizing a multi-scale channel attention approach, successfully integrates features across different scales, enhancing the detection accuracy for minor targets amidst intricate backgrounds. The subsequent comparative experiments indicate that other algorithms experience various missed detection issues in scenes involving local feature loss, whereas our algorithm, based on the OAAM and dynamic detection head, strengthens the connections between channel relationships in partially occluded regions, achieving better detection performance across all scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**IRMT-UAV**} & \multicolumn{4}{c}{**IRSTD-1k**} \\ \cline{2-7}  & **Precision** & **Recall** & **mAP50** & **Precision** & **Recall** & **mAP50** \\ \hline MDvsFA & 60.8\% & 50.7\% & 59.7\% & 55.0\% & 48.3\% & 47.5\% \\ YOLOSR-IST & 56.8\% & 68.4\% & 41.5\% & 41.5\% & 47.0\% & 44.1\% \\ ACM & 76.5\% & 75.2\% & 74.7\% & 67.9\% & 60.5\% & 64.0\% \\ ISNet & 82.0\% & 84.7\% & 83.4\% & 71.8\% & 64.1\% & 65.8\% \\ ALCNet & 84.8\% & 78.0\% & 81.3\% & 83.9\% & 65.6\% & 72.9\% \\ EFLNet & 88.2\% & 84.7\% & 86.8\% & 85.4\% & 70.8\% & 73.8\% \\ DNANet & 89.1\% & 85.2\% & 87.9\% & 84.3\% & 72.1\% & 74.4\% \\ Ours & 92.8\% & 89.5\% & 91.7\% & 86.7\% & 74.0\% & 75.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: A numerical analysis comparing the performance of various techniques on the IRMT-UAV and IRSTD-1k data collections.  
Figure 7: Comparison of the performance of different algorithms for target detection in highly cluttered backgrounds. The blue patch in the upper right corner of the first row represents a magnified detail of the drone target in the local image. Orange circles represent correctly detected targets, purple circles indicate false alarms, and blue circles denote missed detections.