## 4 Experiments and Analysis
### Ablation Experiments
#### 4.4.3 The Impact of the OAAM and Dy-Head on the Network  
In order to examine how the OAAM and Dy-Head affect the recall rate in the detection models, we executed four series of comparative tests by progressively incorporating these two modules into the foundational model. The first group used only the basic detection model. The second group added the OAAM, the third group added the Dy-Head, and the fourth group added both the OAAM and the Dy-Head. The selected application scenario included the detection of faint targets in semi-occluded scenes, and the results are shown in Figure 10. Findings from the experiments reveal that, in the chosen situations, the fundamental model invariably showed instances of undetected events. The OAAM, through feature learning in locally occluded regions, demonstrated a higher tolerance for positional deviations in targets with missing local features, enabling the detection of targets in semi-occluded scenes. However, as the target scale further decreased, missed detections also occurred. The Dy-Head was able to accurately detect targets with significant scale variation but also showed a certain degree of missed detection. When both the Dy-Head and OAAM were incorporated into the algorithm, the model achieved significantly improved detection performance across all scenarios, leading to a substantial increase in recall rate.  
Our model currently operates at 70.8 GFLOPS, with an inference time of 12.8 ms on a single NVIDIA RTX 3090 GPU. Including preprocessing and postprocessing steps, the overall algorithm achieves a frame rate of 48 FPS. These metrics reflect the computational demands of our approach, which prioritizes enhancing detection accuracy and reducing false alarm rates in complex infrared scenarios. Our primary focus has been on improving the model's performance in challenging environments rather than optimizing for lightweight deployment. We acknowledge that reporting on model speed and resource usage is valuable for assessing practical applicability and robustness. We plan to explore  
Figure 9: The feature maps generated by different feature fusion methods: “baseline” refers to the direct concatenation of feature maps from different scales, while the last column indicates cross-scale feature fusion using the iAFF.  
avenues for reducing the theoretical computational load of our model to achieve frame rates above 50 FPS. Additionally, we are considering deploying our model on embedded hardware systems to further evaluate its efficiency and suitability for real-world applications. Our current research involves infrared imaging of drones maneuvering through occluding elements like branches and mesh structures at long distances. The difficulty of precisely controlling the pixel-level movement of drones in the image makes it challenging to establish controlled occlusion percentages. We acknowledge the importance of studying the OAAM under such conditions and are actively exploring ways to construct datasets that allow for precise control over occlusion percentages in future work.  
Figure 10: The study investigates the impact of the OAAM and Dy-Head on recall performance. “GT” represents the ground truth labeled images. “Baseline” refers to the model without using either the OAAM or the Dy-Head. “OAAM” indicates the model with only the OAAM applied. “Dy-Head” refers to the model with only the Dy-Head module applied. “OAAM&Dy-Head” represents the model with both modules applied. Orange circles represent correctly detected targets, purple circles indicate false alarms, and blue circles denote missed detections.