Two‐Stage Approach to Small‐Object Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Unmanned aerial systems (UAS) are increasingly finding applications in civilian and commercial sectors. The utilization of machine learning techniques in UAS image analysis significantly advances target detection and tracking algorithms. In the field of systems engineering, the integration of advanced object detection techniques within UAS represents a pivotal advancement. However, existing object detection and tracking systems encounter challenges when applied to aerial object detection, primarily due to the rapid changes and rotations of obstacles within the UAS's field of view during flight. This paper proposes a fast and accurate real‐time small object detection system based on a two‐stage architecture. Our solution addresses the challenges of small object detection by integrating traditional target detection with deep learning techniques. Specifically, it employs conventional background subtraction and deep learning algorithms to obtain initial detection boxes. Subsequently, we utilize target tracking techniques to refine and enhance the accuracy of the final detection results. By seamlessly integrating traditional and deep learning methods within a two‐stage architecture, our system effectively captures the dynamic nature of UAS flights, demonstrating improved accuracy and efficiency in small object detection. We evaluated our approach on small object datasets, and experimental results show that the proposed method enhances aerial object detection performance compared to conventional approaches. This research contributes to ongoing efforts to advance UAS applications across various domains. And by demonstrating the efficacy of our integrated approach, this research underscores the role of systems engineering in enhancing UAS capabilities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it