## 3 Results
It can be calculated using the following equation:  
\[Recall\ =\ \frac{TP}{TP+FN} \tag{5}\]  
The F1 score provides a balanced harmonic mean of precision and recall, offering a single metric that accounts for both false positives and false negatives. It can be calculated with the following equation:  
\[F1\ score\ =2\times\ \frac{Precision\times Recall}{Precision+Recall} \tag{6}\]  
An example of SARGAS detection during the first inference test is presented in Figure 9. The top image illustrates a false negative, where the object of interest was not detected. This omission likely occurred due to the individual's shape and orientation closely resembling that of a nearby wooden branch--an object frequently encountered in natural, snow-covered environments. The bottom image includes both a true positive and a false positive detection. While the true positive is clearly localized and confidently classified, the false positive highlights one of the system's key challenges: confusing background elementsthat mimic human-like silhouettes from an aerial perspective. Such errors are particularly common in winter scenes, where snow, shadows, and environmental features like rocks, stumps, or debris contribute to false alarms. Following the analysis of accuracy, precision, recall, and F1 score, the full confusion matrix for each evaluated algorithm is shown in Table 2. The evaluation was conducted on video footage that partially overlaps with the training set. In addition, the corresponding performance metrics derived from the confusion matrix values are summarized in Table 3, providing a detailed view of each algorithm's detection quality. To quantify the impact of the secondary classifier in the SARGAS pipeline, we conducted an ablation test by disabling the second-stage filtering step. The results show that removing the secondary classifier leads to a dramatic increase in false positives--from 614 to 11,577--despite a slight gain in recall. This causes precision to drop sharply to just 10.46%, with a corresponding F1 score of only 18.84%. These findings confirm that the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **True Positives** \\ **(TPs)** \\ \end{tabular} & \begin{tabular}{c} **False Positives** \\ **(FPs)** \\ \end{tabular} & \
\begin{tabular}{c} **False Negatives** \\ **(FNs)** \\ \end{tabular} &
\begin{tabular}{c} **True Negatives** \\ **(TNs)** \\ \end{tabular} \\ \hline SARGAS & 1314 & 614 & 108 & 16 \\ SARGAS without classifier & 1352 & 11,577 & 70 & 0 \\ YOLOv5 & 1362 & 54 & 60 & 16 \\ Faster-RCNN & 1373 & 203 & 49 & 16 \\ cutLER & 0 & 0 & 1422 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithm performance check on similar to training data video (confusion matrix). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline SARGAS & 65\% & 68.16\% & 92.42\% & 78.43\% \\ SARGAS without classifier & 10.40\% & 10.46\% & 95.08\% & 18.84\% \\ VOLov5 & 92.4\% & 96.19\% & 95.8\% & 96\% \\ Faster-RCNN & 86.95\% & 87.14\% & 96.58\% & 92.94\% \\ cutLER & 1.11\% & 0\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm performance check on similar to training data video (core metrics). Figure 9: First inference test: example FN (**upper picture**), TP, and FP (**lower picture**). secondary classifier plays a critical role in maintaining detection precision and overall system reliability by acting as a soft validation layer that suppresses noisy activations from the primary detector. As shown in Table 3, the YOLOv5 algorithm demonstrates higher accuracy compared to the custom SARGAS algorithm. This outcome highlights the advantage of supervised learning, particularly when the inference data closely resembles the training data. However, this performance benefit diminishes when inference is conducted on a different video sequence that bears no similarity to the training set.