AviSR-Pose: Aircraft Pose Estimation via Large-Receptive-Field Super-Resolution and Keypoint-Aware Transformer
Bibliographic record
Abstract
This paper presents a novel Transformer-based framework for precision aircraft pose estimation in low-resolution airport surveillance scenarios, integrating synergistic super-resolution reconstruction and oriented keypoint detection. Addressing the challenges of limited feature discernibility in small aircraft and background complexity, we propose: (1) Aircraft SRNet, a cross-cascaded upsampling-downsampling network leveraging Transformer's global modeling to enhance resolution while mitigating quantization errors; (2) Aircraft PoseNet, featuring a lightweight Transformer backbone with Large receptive field-augmented decoding for heatmap-based keypoint localization. Evaluated on the curated Aircraft-KP dataset, our method achieves state-of-the-art performance (85.9% AP), surpassing ViTPose and YOLOv8-pose in keypoint detection accuracy. The framework significantly improves tail keypoint accuracy and offers practical value for digital twin airports and collision prevention.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".