## 4 Discussion  
The results of this study have exceeded initial expectations, particularly given that few existing object detection systems rely on a binary classifier as their primary detection mechanism. The original intent was to enhance motion detection using traditional optical flow algorithms, such as the Lucas-Kanade or Horn-Schunck methods, in combination with background subtraction. However, these approaches proved ineffective in the context of simultaneous motion of both the camera drone and the object of interest. The application of affine transformations in this setting further exacerbated the issue, introducing distortions that undermined reliable motion differentiation. The novelty of the proposed SARGAS framework lies in its hybrid, weakly supervised detection pipeline, which integrates a custom-designed binary CNN as a region-free detector, followed by a secondary CNN to validate predictions. Although both CNNs are trained on the same weakly labeled dataset of cropped human and non-human samples, they serve complementary and sequential roles: the first CNN performs dense, grid-based inference across the entire frame, while the second operates only on candidate regions to refine predictions through an accept/reject decision. Unlike conventional semi-supervised methods that rely on bounding box proposals, pseudo-labeling, or pretrained detection backbones, SARGAS uses lightweight folder-based supervision and sliding window inference without requiring any bounding box annotations. The second classifier functions as a soft filter that improves precision without the need for handcrafted heuristics or thresholding. In addition, SARGAS fuses motion cues from background subtraction and affine-aligned frame differencing, allowing it to adapt to camera motion and unstable aerial footage--typical limitations in drone-based object detection. To our knowledge, no prior method integrates this combination of region-free detection, dual-CNN verification, and motion-aware refinement under weak supervision in a single unified pipeline for small object detection from aerial imagery. Conversely, the Mixture of Gaussians (MOG2) background subtraction method demonstrated surprisingly robust performance. While it did not introduce additional false positives, it successfully detected objects of interest in instances where the sliding window method failed. Nevertheless, the MOG2 approach comes with a notable limitation: it performs optimally when the camera maintains a top-down perspective. Detection quality degrades substantially when the camera captures side views, and performance is poorest in front or rear views where motion is minimal, and the subject size exceeds the sliding window capacity. Under such conditions, both detection branches face challenges in reliably identifying human subjects. When benchmarked against a supervised object detector such as YOLOv5, the SARGAS system does underperform in terms of overall detection metrics. However, this comparison must be contextualized: YOLOv5 benefits from extensive supervised training and large annotated datasets, while SARGAS relies on an unsupervised learning paradigm and minimal manual input. Given the scarcity of existing object detectors that train effectively on unlabeled data, the development and deployment of SARGAS represent a meaningful contribution to the field. The observed rate of false positives highlights an area requiring further investigation. Future work will focus on refining the postprocessing pipeline, enhancing classification accuracy, and improving overall reliability. Despite its current limitations, SARGAS demonstrates promising adaptability and potential for improvement. With further optimization and access to more powerful computational resources, we strongly believe that unsupervised CNN-based object detectors may eventually rival or even surpass their supervised counterparts in both flexibility and performance.