## 3 Results
This effect is illustrated in Figure 10, where a randomly selected set of nine images from both evaluation tests (YOLOv5 and SARGAS) is presented to demonstrate their respective performance in practical scenarios. The results of the inference test conducted on video footage unrelated to the training data are presented in Table 4. Although the scene is visually simple--comprising primarily of snow with minimal background clutter--this test case is designed to highlight a key limitation of fast, supervised algorithms such as YOLOv5, which may overfit to training distributions. In contrast, the test also demonstrates the generalization strengths of our custom, unsupervised algorithm under minimal-scene conditions. The performance metrics derived from the second video and the associated prediction scores demonstrate a clear advantage of our custom algorithm over YOLOv5 when applied to previously unseen data. Unlike YOLOv5, which relies heavily on supervised learning and exhibits notable performance degradation under domain shift, our algorithm--trained in a partially unsupervised manner--learns feature patterns autonomously and generalizes more effectively. This suggests enhanced robustness in real-world scenarios where input \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **True Positives** & **False Positives** & **False Negatives** & **True Negatives** \\  & **(TPs)** & **(FFs)** & **(FNs)** & **(TNs)** \\ \hline SARGAS & 1275 & 59 & 161 & 0 \\ YOLOv5 & 417 & 162 & 1019 & 0 \\ Faster-RCNN & 1435 & 4 & 1 & 0 \\ cutLER & 0 & 0 & 1436 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Algorithm performance check on a video unrelated to the training data. Figure 10: YOLOv5 (**left**) and SARGAS (**right**) performance check on second video. data may deviate from the training distribution. Additionally, it is worth noting that YOLOv5 retains all predicted bounding boxes along with their confidence scores, whereas our algorithm employs a post-classification filtering step that discards all detections below a confidence threshold of 0.85, thereby reducing false positive noise. The evaluation conducted on footage unrelated to the training data reveals significant differences in the generalization capabilities of the tested algorithms. As shown in Table 5 and the corresponding confusion matrix, the SARGAS algorithm maintains a strong balance between precision (95.58%) and recall (88.79%), achieving an F1 score of 92.06%. These results indicate that SARGAS retains robust detection performance under domain shift, with relatively few false positives (FPs = 59) and a manageable number of false negatives (FNs = 161) out of 1436 possible detections. In contrast, YOLOv5--while performing well on data resembling its training distribution--struggles when applied to novel data. Its recall drops sharply to 29.04%, resulting in more than 1000 missed detections (FNs = 1019), despite maintaining moderate precision. The resulting F1 score of 41.39% reflects reduced practical applicability in scenarios where the test environment diverges from training conditions. Faster-RCNN achieves near-perfect results, with a precision of 99.7%, recall of 99.9%, and an F1 score of 99.8%, detecting nearly all objects with minimal false detections (FPs = 4; FNs = 1). This reflects excellent robustness and generalization, although the computational cost and longer inference time may hinder its use in time-sensitive or resource-constrained environments such as onboard drone systems. Meanwhile, cutLER fails to detect any objects in the test footage (TPs = 0; FNs = 1436), revealing its limited capability in generalizing without prior domain adaptation. As an unsupervised method, it may require further tuning to be viable in real-time search and rescue operations. While these results highlight SARGAS's stability under domain shift, they also reveal an elevated rate of false positives that warrants closer examination.