## 2 Related Works
### Infrared Image: Context and Challenge for Detection  
Drone detection in infrared images is a research area that has garnered considerable attention due to increasing concerns regarding security and privacy. This type of imaging facilitates detection under low-visibility conditions, such as at night or during adverse weather, during which traditional optical cameras may be ineffective. However, this approach presents unique challenges that necessitate the application of advanced artificial intelligence and deep learning techniques.  
Drones exhibit a thermal signature that can vary significantly depending on factors such as altitude, speed, and ambient temperature. In the following sections, some of the primary challenges are detailed:  
* Thermal Noise: In complex environments, other objects, such as animals, vehicles, or even parts of the terrain, can emit thermal radiation, creating \"noise\" that can interfere with the accurate detection of drones. Models must be able to distinguish between these heat sources and the drone, which is often a small object with low thermal emissions.
* Temperature Variability: The temperature of drones can change during flight due to factors such as altitude and speed, which can complicate detection. Furthermore, ambient temperature, which can vary significantly between day and night or between different seasons, affects the effectiveness of detection algorithms.
* Resolution and Distance: The quality of infrared images is greatly dependent on the resolution of the camera and the distance from the target. At greater distances, the drone's thermal signature may become indistinguishable from the background, posing a significant challenge for detection models.
* Cluttered Environments: In scenarios where many objects are present (e.g., in urban areas), it is crucial for models to accurately identify and track the drone among other elements that may be emitting heat.  
In response to these challenges, the Beijing Institute of Electronic Equipment has undertaken significant efforts to advance drone detection technologies by organizingseveral workshops during the Conference on Computer Vision and Pattern Recognition (CVPR) in 2020, 2021, and 2023. These workshops have focused on the development of advanced models capable of detecting drones within a database of 600 videos, which have been segmented into frames of infrared images.  
Among the most innovative proposals, [PERSON] et al. developed a method for detecting tiny objects in videos, guided by spatio-temporal motion information [61]. This approach, tested on the Anti-UAV 2021 dataset, showed a significant improvement in drone detection in complex scenarios, outperforming other traditional small object detection methods.  
On the other hand, [PERSON] et al. introduced UTTracker [51], a transformer-based model designed for tracking drones in thermal infrared videos. This model effectively addresses challenges such as variations in target appearance, frequent disappearances, and camera movement, enabling it to achieve competitive performance in the latest 2023 Anti-UAV challenge, where it secured second place overall.  
In the same workshop, a model based on YOLOv8 and DINO (Self-Distillation with No Labels) [62] was presented, achieving a significant breakthrough by attaining a score of 69.7% on the complete dataset. Due to these results, the model secured first place in the competition, establishing itself as the state of the art in drone detection using infrared images and representing the most accurate model to date [63]. Consequently, this model is selected as the baseline against which other results will be compared.