Small-Object Detection for UAV-Based Images
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned aerial systems (UAS) are increasingly being deployed in civilian and commercial areas. The application of machine learning in UAS image analysis greatly promotes the progress of target detection and tracking algorithms. However, current object detection and tracking system algorithm can hardly be applied to detect aerial targets. Because the view of UAS changes and rotates quickly during the flight. In this paper, we propose a fast and accurate real-time small object detection system based on a two-stage architecture. The proposed addresses the small object detection challenges by combining the traditional target detection with deep learning. More precisely, it uses conventional background subtraction and deep learning algorithm to get the initial detection box, and then use target tracking to get the final result. We evaluated our approach on the small object data sets. Experimental results show that the proposed method has improved the aerial object detection performance compared with other conventional approaches.
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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