A Novel Keypoint Supplemented R-CNN for UAV Object Detection
Bibliographic record
Abstract
Aerial imagery and remote sensing applications are excellent examples of environments that challenge current deep learning-based object detection architectures due to the images generally consisting of small objects within large, complex backgrounds. In this article, the region proposal network (RPN) of the successful Mask region-based convolutional neural network (R-CNN) architecture is supplemented with a second nonconvolutional branch to increase the number of accurately predicted regions of interest (RoIs). The additional RoIs are predicted using keypoint features unique from the Mask R-CNN backbone which better capture the existence of small objects and allow for a set of RoIs to be predicted independent of the standard Mask R-CNN feature pyramid network (FPN). The proposed architecture is evaluated on synthetic data to demonstrate the improved performance of the supplemented RPN on smaller objects and objects of changing scale. The two-branch network is then demonstrated to achieve improved detection accuracy when applied to the challenging aerial-cars and vehicle-detection datasets (VDDs).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".