## 4 Results
### Discussion  
The results obtained in this study reveal a range of behaviors across the evaluated models (YOLO, GELAN, DETR, and ViTDet), highlighting their respective strengths and weaknesses in the detection of drones in thermal images. The main findings are interpreted as follows:  
* **YOLO**: This model excels in terms of speed and inference time, being the fastest of the four with 134 FPS and an inference time of 7.5 ms. However, its precision (mAP 50:95 of 72%) is slightly lower than that of GELAN and DETR, indicating that although YOLO is suitable for real-time applications, it may not be the best choice when precision is critical. * **GELAN**: GELAN stands out for its precision, achieving the highest mAP 50:95 (77%) and an AP 50 of 81%. Its performance is particularly notable in scenarios with dynamic background noise (DBC) and Fast Motion (FM), where other models struggle. Despite \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **IoU** & **TNR** & **F1-Confidence** \\ \hline YOLO & 0\% & 0.6\% & 1\% at 0.027 \\ \hline GELAN & 0\% & 0.5\% & 2\% at 0.010 \\ \hline DETR & 0\% & 3\% & 5\% at 0.009 \\ \hline ViTDet & 100\% & 10\% & 12\% at 0.415 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results for video type Out Of View (VE). Figure 11: Detection results for Out Of View (VE). its superior precision, its speed is moderate (95 FPS), making it a viable option in applications where precision is prioritized over speed. * **DETR**: DETR demonstrates balanced performance with a mAP 50:95 of 75.7% and an AP 50 of 77.3%, positioning itself between YOLO and GELAN in terms of precision. However, its inference time is significantly longer (26 ms), making it less suitable for real-time applications. DETR proves to be particularly effective in detecting drones in complex situations such as Scale Variations (SVs) and thermal-infrared crossovers (TCs). * **ViTDet**: Despite its promising results in similar tasks in other contexts, ViTDet has the lowest performance in this study, with a mAP 50:95 of 62% and an AP 50 of 65%. Its inference time is also the slowest (28 ms), which, combined with its lower precision, suggests that this model may not be the most suitable option for drone detection in thermal images under the evaluated conditions. In particular, it is the best model to recognize when not to detect a drone in an image, with an accuracy of approximately 9% in such cases (VE), compared to around 1% for the other models. Taking into account the various scenarios, one may conclude the following:
* **UAV Category**: All models perform well in this category, and DETR achieves the highest precision (90%). This reflects the models' ability to handle standard scenarios without additional complicating factors. * **Target Scale (TS)** and **Scale Variation (SV)**: Despite having lower precision across all examples, GELAN stands out slightly in these groups, highlighting its ability to manage significant variations in the target size. In particular, all models have similar precision in both groups, except for YOLO, which does not benefit when the Scale Variation is small to large (TS). * **Dynamic Background Clusters (DBCs)**: Although the GELAN performance in the example is lower, it shows consistent performance for this type of video. However, the difference between models in this category is not as pronounced as in others. * **Thermal and Infrared Crossover (TC)**: DETR and GELAN are the most effective models, suggesting that they are better at handling images with multiple objects of similar temperature, which tends to confuse YOLO and ViTDet. This is the category in which the difference between the models is the least noticeable. * **Fast Motion (FM) and occlusion (OC)**: GELAN also performs well in tracking drones in fast-motion scenarios and under occlusion, making it a robust option for more challenging environments. * **Out of View (VE)**: In this case, ViTDet is clearly the best model, being the only one with a significant percentage, reaching 10%.