## 4 Results Discussion
### Computer Vision Module Results  
The first aspect to analyze was whether the trained model detects UAVs effectively. In the YOLOv10 training graphs, the results appeared highly promising, but these were obtained using static images. To determine if the model truly performs well for the  
Figure 12: Time required for UAV detection as a function of distance. developed system, it was essential to test it with real-time images in environments where the system could be deployed. For this reason, Figure 13 illustrates the success rate when a UAV was flown at different distances from the camera. The goal was to verify whether the model detects UAVs properly and to analyze how distance affects detection. As can be seen from the results, the overall detection performance was very good. The detection rate was always above 75% for distances up to 50 m. At closer distances, the UAV was always detected, with the percentage decreasing as the distance increased, but strong results were maintained throughout. It is important to note that the results for this parameter, as well as for the others, were obtained from performing different tests in various environments to ensure the reliability of the results. This prevented skewed data that might arise from testing in the same location repeatedly. It is also important to indicate that the tests were conducted under conditions where the UAV could be detected, avoiding flights in darkness or in scenarios with no visibility where even a human observer would not be able to identify the UAV. Nonetheless, the results were very positive considering the characteristics of the UAVs used, which were small devices that can easily blend in with the environment. The next aspect to analyze was the effectiveness of the tracking algorithm, evaluating whether the PTZ camera can continuously follow the UAV as it moves through the environment. Once again, the impact of distance on the tracking process was analyzed. Figure 14 presents the results of the tracking algorithm. As can be observed, the success rate no longer followed the same trend with respect to distance. The best results were obtained at intermediate distances, with the tracking success rate being above 90% for distances of 20 and 30 m and around 83% for 40 m.  
The poorest results were obtained at the shortest and longest distances, but both cases have a clear explanation. At shorter distances, the quality of tracking decreases because the angular velocity needed to follow the same movement of the UAV is greater compared to longer distances. A linear displacement of the UAV at close range results in a more significant angular change for the camera, which can make tracking more challenging. At a distance of 50 m, the tracking effectiveness decreased because the model struggled more to detect the UAV, as seen in Figure 14. At that distance, the detection of the UAV by the camera was not consistent, which affected the tracking algor  
Figure 13: Detection test results using the computer vision module as a function of distance from the UAV. the UAV is not detected, the algorithm lacks the necessary visual reference, reducing the smoothness of the tracking. Consequently, it can be concluded that the tracking algorithm functions optimally as long as the UAV is detected. The reduction in the tracking rate at greater distances is primarily due to the loss of continuous detection of the UAV, indicating that the smoothness of tracking heavily relies on the model's ability to consistently detect the UAV. Lastly, in terms of localization, the goal was to verify if the algorithm can accurately send the exact position of the UAV to the PTZ camera. For these tests, the distance was not a factor to consider since, if the algorithm is correctly developed, once the UAV is detected by the fixed cameras, the position will be triangulated independently of the distance. The only aspect affected by distance is detection, as the UAV must be detected by at least two cameras to be localized.