## 2 Related Works  
### Cutting-Edge Detection Models  
Convolutional neural networks (CNNs) and transformer-based neural networks (TNNs) are the two most recognized architectures in object detection. The former represents a type of artificial neural network specifically designed to process data with a grid-like structure, such as images [52; 53]. In contrast to traditional neural networks, CNNs are optimized to capture spatial and hierarchical patterns in images, which renders them particularly suitable for computer vision tasks, including image classification, object detection, and segmentation. Building upon CNNs, more advanced and complex models have been developed to enhance accuracy and efficiency in object detection. For this study, the most promising models from such architectures have been selected:  
* You Only Look Once (YOLO) [54; 55] is designed to detect and locate objects in images or videos in real-time. Unlike other methods that perform detection in multiple stages, YOLO integrates object detection and localization into a single neural network. This approach enables faster processing by performing object detection in a single pass through the image via the neural network. Currently, YOLO is considered the state of the art for single-pass detection methods. Due to its capability to effectively detect fast-moving objects by performing a single pass, it is well suited for handling fast-moving drones. * GELAN (Generative Enhanced Low-light Adversarial Network) uses a multi-layer architecture capable of extracting both high-level semantic features and fine-grained details, which is essential for detecting objects in low-light, low-contrast environments such as thermal imaging. This network uses spatial pyramid clustering and layer aggregation to improve feature representation, enabling it to recognize objects at different scales and resist visual interference such as thermal noise or complex backgrounds. The application of GELAN networks in the context of drone detection in thermal imaging is particularly interesting because of their advanced ability to handle difficult visual conditions as demonstrated in their medical imaging applications [56]. In the specific case of drone detection in thermal imaging, the specific attributes of GELAN can help differentiate drones from other heat-emitting objects, a challenge often amplified in thermal imaging. In addition, the efficient processing of GELAN of multiscale and occluded objects is well suited to the context of drone detection, where drones may vary in size, speed, or visibility. On the other hand, transformer-based neural networks (TNNs), commonly known as transformers, represent a class of neural network models that have revolutionized the field of machine learning, particularly in natural language processing (NLP) and, more recently, in computer vision [57; 58]. In the domain of computer vision, these models have begun to rival and even surpass traditional convolutional neural networks (CNNs) in several tasks, owing to their capacity to capture global relationships within the data and their scalability. However, their application poses challenges, particularly with respect to the computational and data requirements. As with CNNs, the models selected for this study are as follows:  
* Detection Transformer (DETR) [59] is designed to perform object detection and instance segmentation in a more efficient and accurate manner. Unlike traditional CNN-based methods, DETR employs the transformer architecture, which has proven to be highly effective in natural language processing tasks, to address problems in computer vision. DETR is particularly useful in environments where conditions are highly variable (different temperatures, weather, and times of day). Its ability to capture more abstract and complex patterns in images allows it to enhance drone detection in scenarios with changing thermal conditions or when drones have thermal signatures similar to their surroundings.