## 5 Conclusions  
The primary objective of this study--developing a novel object detection pipeline centered around a binary classifier--has been successfully achieved. The proposed SARGAS framework introduces a hybrid, weakly supervised system that leverages a custom binary CNN for region-free detection, supported by a secondary classifier for false positive suppression. While still in early development, the system demonstrates significant potential for further refinement and real-world application.  
In complex scenarios where objects may blend into the environment, SARGAS achieved a recall of 92.42%, with moderate precision (68.16%) and accuracy (65.00%), using minimal supervision and grayscale Canny-augmented training data. When evaluated on a test video unrelated to the training set, SARGAS exhibited strong generalization capabilities--outperforming YOLOv5 by 23.56% in precision and achieving an accuracy of 85.28%, compared to YOLOv5's 26.10%. This suggests that SARGAS is well-suited for dynamic environments where labeled data is scarce or unavailable.  
However, several limitations must be acknowledged. Firstly, it is shape-driven misclassification, because SARGAS operates on grayscale edge-augmented imagery, it remains insensitive to color but may be distracted by environmental features that mimic the target class in shape or contour (e.g., tree trunks, rocks, or debris).  
Another limitation to address is fixed operation altitude--the system relies on a sliding window with a predefined receptive field. To ensure reliable detection, SARGAS must operate within a specific flight altitude range (e.g., 12-25 m). Objects too small or too large relative to the window can lead to missed detections or increased false positives.  
And lastly, training scalability is a limitation. Although these issues can be mitigated by expanding the training dataset to include more diverse examples, doing so may introduce additional false positives and would require significantly greater computational resources to retrain the model effectively.  
Despite these challenges, the current results confirm the viability and adaptability of the proposed method. SARGAS offers a promising foundation for lightweight, weakly supervised object detection in aerial imagery, with clear pathways for improvement through dataset expansion, architectural optimization, and deployment tuning. Future work will focus on reducing false positives, increasing robustness to viewpoint variation, and optimizing runtime performance for real-time deployment.  
**Author Contributions:** Conceptualization, G.V. and I.S.; methodology, G.V.; software, G.V.; validation, I.S., formal analysis, G.V.; investigation, G.V.; resources, G.V.; data curation, G.V.; writing--original draft preparation, G.V.; writing--review and editing, I.D.; visualization, G.V.; supervision, I.D.; project administration, I.D.; funding acquisition, I.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.  
**Acknowledgments:** Main support and materials were obtained from Antanas Gustaitis' Aviation Institute, Vilnius Gediminas Technical University.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
Figure A4: Relu accuracy and loss over epochs, subset 3.  
Figure A5: Relu accuracy and loss over epochs, subset 4.  
Figure A6: Swish accuracy and loss over epochs, subset 0.  
Figure 13: Yolov5 training over epochs, subset 2.  
Figure 14: Yolov5 training over epochs, subset 3.