## 2 Related Work  
The algorithms used for UAV detection, whether through RF signals or computer vision, have evolved over the years due to the specialization and development of UAVs, which necessitate increasingly novel, effective, and robust algorithms and systems to counter the threat of UAVs being used for malicious purposes. Regarding the algorithms used for detection through computer vision, object detection algorithms are primarily employed, as these techniques use neural networks to locate and classify objects in an image. This is the goal when using cameras: to identify UAVs from real-time video feeds. These algorithms have continuously improved, always building upon CNNs, which are networks capable of recognizing and learning features and patterns in images [12]. Initially, two-stage detector algorithms were used, including the R-CNN, which has three steps: feature extraction by generating different regions to be analyzed in the image, running a CNN on each of these regions, and finally applying an SVM and a linear regressor to classify the object [13]. An evolution of this algorithm is the Faster R-CNN, which is faster because it generates regions more efficiently by introducing the region proposal network (RPN) and unifying the algorithm's stages [14]. Another object detection technique involves one-stage detectors, which are simpler and faster than two-stage detectors while maintaining a high level of effectiveness and accuracy, making them more suitable for real-time applications. Among them is the Single Shot Multibox Detector (SSD), presented by [PERSON] et al., which achieves very good results in terms of precision for small objects by performing localization and classification tasks in a single step [15]. Finally, the most advanced object detection algorithm currently, also a one-stage detector, is the You Only Look Once (YOLO) algorithm, an algorithm introduced by [PERSON] et al. where the image is divided into different regions, and bounding boxes are predicted in each region along with their probabilities [16]. The YOLO algorithm has continuously advanced with each version, enhancing detection performance, speed, and accuracy. These improvements have proven valuable in UAV detection applications across different scenarios [17; 18; 19]. The version used in this paper, YOLOv10, introduces two major advancements compared to previous versions: a Consistent Dual Assignments mechanism for NMS-free training and an Efficiency-Accuracy-Driven Model Design. These innovations include a lightweight classification head, spatial-channel decoupled downsampling, and rank-guided block design, which enhance both accuracy and efficiency, as explained by [PERSON] et al. [20]. YOLOv10 demonstrates superior accuracy and efficiency compared to previous YOLO versions and other advanced models, making it highly suitable for real-time detection tasks. As demonstrated by [PERSON] et al. in his article, YOLOv10 achieves the highest accuracy (COCO AP%) with lower latency, which underscores its advantages in both precision and speed for real-time applications. When it comes to tracking a detected object, [PERSON] et al. presented an algorithm for the dynamic tracking of UAVs using deep learning techniques. Once the object is identified with YOLOv3, a PTZ camera follows the UAV based on its relative position, the PTZ angle, and the coordinates of its position in the image [21]. On the other hand, [PERSON] et al. performed tracking using motion detection techniques with a Kernelized Correlation Filter (KCF) and Discriminative Correlation Filter (DCF). They also achieved 3D localization of a UAV by triangulating its position from the images captured from different viewpoints, successfully determining the object's spatial coordinates [22]. In terms of identification through radio spectrum analysis techniques, there are several methodologies and tools employed, as well as a variety of neural network algorithms, for UAV detection, each with specific characteristics and limitations. [PERSON] et al.