## 5 Conclusions  
Throughout this article, we have thoroughly explained the two UAV detection methods developed as a potential solution to the growing security concerns stemming from the misuse of UAVs in recent times. The two developed methods are designed to complement each other, creating a more effective and comprehensive detection system using artificial intelligence algorithms, making this system a novel and intelligent solution. We designed the first detection method based on radio-frequency signals, which are captured and processed by the SDR BladeRF 2.0 micro xa4 (Nuand LLC, Rochester, NY, USA), along with an NVIDIA Jetson Nano Developer Kit (NVIDIA Corporation, Santa Clara, CA, USA) responsible for running the detection algorithm. The developed algorithm is based on autoencoders, which, as discussed, are unsupervised neural networks frequently used for anomaly detection. In this project, multiple autoencoders were trained, each for a specific frequency band to be analyzed. The input data consisted of 2048 power values that made up the 100 MHz bandwidth of each band. A validation system was developed based on the autoencoders, in which a set of conditions must be met to confirm the presence of a UAV, aiming to balance detection time with a reduction in false positives. Figure 15: Vision of the cameras in the localization tests. In **(a)**, it is shown that the fixed cameras simultaneously detected the UAV, while in **(b)** it is shown that the PTZ camera had already located the UAV after being provided with its exact position. The second method involves a module comprising four fixed cameras and a PTZ camera, connected via IP addresses to an NVIDIA Jetson Orin Nano responsible for managing them. Each camera detects UAVs using a custom-trained YOLOv10 model with over 60,000 images, showing excellent performance in detecting UAVs even at long distances. The computer vision module also includes the function of tracking the UAV continuously. An algorithm was developed for tracking, ensuring the PTZ camera keeps the UAV centered in the image. Additionally, a localization system for the UAV was implemented, allowing the PTZ camera to track the UAV once two or more fixed cameras simultaneously detected it, calculating the exact position and sending it to the PTZ for tracking. Both modules are interconnected and communicate in real time to ensure UAV detection. The developed system also includes a communication protocol using SIA DC-09-2021 (Security Industry Association, Silver Spring, MD, USA), allowing the system to alert a remote monitoring station (CRA) when a UAV is detected, and periodically sending videos that enable them to monitor the UAV's presence and take appropriate actions. The results indicate that both modules performed well in terms of detection, achieving high success rates even at distances beyond 40 m. The system's primary goals are to detect efficiently while minimizing false positives. This is achieved in the camera module by implementing techniques that filter out a significant portion of false positives. In the SDR module, with an adequate database, the percentage of false positives remains minimal. Both localization and tracking algorithms functioned appropriately, accurately locating the UAV when it was detected by two cameras, and the tracking algorithm successfully maintained continuous UAV tracking. Future improvements could mainly focus on expanding the datasets for both modules and upgrading the hardware to more powerful devices. With a more extensive and varied database, YOLOv10 detection could be further improved, enhancing accuracy and reducing false positives. In the SDR module, a more comprehensive initial dataset of non-UAV signals used to train the autoencoders would result in better signal reconstruction when no UAV is present, further improving detection accuracy. Regarding hardware, this has been the main limitation of the system, as both NVIDIA devices were operating at their maximum capacity.