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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".