Robust Crane Pose Estimation using High-Resolution Drone SAR
Bibliographic record
Abstract
Pose estimation in Synthetic Aperture Radar (SAR) imagery remains a challenging task due to geometric distortions and variations in azimuth and look angles. In this study, we construct a multidirectional SAR target dataset using a drone-based SAR system, capturing diverse azimuth angles, crane boom angles, and incidence angles under different environmental conditions. Leveraging this dataset, we propose a multitask learning framework based on ConvNeXt to simultaneously estimate the target's azimuth and crane boom angles. The model employs a multi-stage training strategy to balance classification and regression tasks, achieving an RMSE of 10.29° and an MAE of 1.86° for azimuth estimation and a Top-1 accuracy of 93.45% for boom angle classification. The results demonstrate the effectiveness of our approach in handling pose estimation challenges in SAR imagery. Our dataset and model provide a foundation for advancing SAR-based automated target recognition and ISR applications.
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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) | 0.000 | 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) | 0.000 | 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 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".