## 3 Methodology
### Metrics  
For the evaluation of the performance of the trained neural models, this study is based on a set of standards for image segmentation, which is described as follows:  
* **Intersection over Union (IoU)**. The IoU is a metric that measures the degree of overlap between the predicted bounding box of the model and the actual bounding box of the object, and is calculated by dividing the overlap area between the two boxes by the overlap area of their union (see Figure 2): \[\text{IoU}=\frac{\text{Intersection area}}{\text{Union area}}\] (1)A higher IoU value indicates a better prediction, as the predicted box aligns more closely with the actual box. In video detection problems, where objects can move and change shape, a high IoU is critical to ensure that models not only detect the presence of an object but also accurately localize it in each frame of the video. * and **F1-Confidence**. The \(F1\)-Score is the harmonic mean between precision and recall. This metric is particularly useful when there is an imbalance between the classes of true positives and false negatives: \[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}}\] (2)
* is the proportion of true positives (correctly detected objects) over the sum of true positives and false positives (incorrect detections). * measures the proportion of true positives relative to the sum of true positives and false negatives (undetected objects). In video object detection, the F1-Score is important, as it balances precision and recall, providing an overall assessment of the model's performance regarding correct detections versus missed detections. The F1-Confidence is an extension of the F1-Score that incorporates the confidence of the model's predictions. This metric adjusts the F1-Score based on the average confidence of correct detections, allowing for the consideration of both the quantity of correct predictions and the certainty with which the model makes those predictions. It is particularly useful in applications where confidence in the model's decisions is critical, such as aerial surveillance or automated detection systems, where an incorrect prediction made with high confidence can have significant consequences. This metric provides a more detailed evaluation of the performance of the model, particularly in scenarios where the quantity of correct detections is important, and the certainty with which those detections are made is equally crucial. AP measures how precision varies at different levels of recall and is derived from the precision-recall curve. AP is typically calculated for a single Intersection over Union (IoU) threshold, usually set at 0.5, which is referred to as [EMAIL_ADDRESS] or AP@50. Consequently, the AP value varies depending on the IoU threshold that is considered acceptable. However, for more complex problems and more rigorous evaluations, the mean Average Precision (\(mAP\)) is utilized. \(mAP\) averages the \(AP\) across several IoU thresholds,  
Figure 2: IoU description. typically ranging from 0.5 to 0.95 in increments of 0.05, referred to as _[EMAIL_ADDRESS]:0.95_ or _mAP@50:95_. This can be calculated as shown in Equation (3):  
\[mAP@50:95=\frac{AP@50+AP@55+\cdots+AP@90+AP@95}{10}=\frac{\sum_{x=0.5}^{0.95}AP@ x}{10} \tag{3}\]  
This metric is particularly relevant in video detection tasks, as it evaluates the ability of a model to detect objects with varying degrees of overlap between the predictions and the ground truth annotations. * **True Negative Rate**. The True Negative Rate (_TNR_) or specificity quantifies the proportion of true negatives (_TN_s) that are accurately identified out of the total number of cases where the target is absent (i.e., the sum of true negatives and false positives): \[TNR=\frac{TN}{TN+FP}\] (4)
* **True Negatives (TNs)**: These are cases where there is no drone in the scene, and the model correctly does not detect any target.