Real-time aircraft bracket junction point detection for split flying vehicle module docking
Bibliographic record
Abstract
The split flying car is composed of a flight module, a passenger capsule and an intelligent chassis module. The autonomous docking between these modules enables the split flying car to switch between flight mode and driving mode. The positioning of the aircraft bracket junction point is crucial for determining the desired position of the flight module. However, the complex and variable takeoff and landing environments and the limited computing power of edge computing platforms pose significant challenges to the perception task. To address these issues, we propose a lightweight network-based aircraft bracket detection model that meets real-time requirements in docking scenarios. Firstly, we use the inverse perspective mapping stitched bird’s eye view as input to obtain the junction point coordinates of the aircraft bracket through the junction point detector. Then the position information of the bracket is obtained by eliminating the mis-detected junction points and reasoning out the missed junction points based on the a priori information of the aircraft bracket. To facilitate vision-based aircraft bracket detection research, a dataset is established, which is the first publicly available dataset in this research field, collecting 4631 bird’s eye views in different environments. The proposed method can achieve FPS of 35.79 and average precision of 0.915 in the Jetson AGX Xavier edge computing platform. The proposed method can also achieve competitive results when applied in parking slot detection with at least 2× faster inference speed. • We propose a junction point-based approach for flying vehicle aircraft bracket detection. The complex aircraft feature detection is converted into a simple junction point detection. The lightweight network and channel pruning make it possible to meet real-time requirements even on edge computing platforms. • A junction point complementation scheme is designed for the aircraft bracket. The known junction points and a priori information are used to effectively exclude the mis-detected junction points and reason out the missed junction points. • A dataset is created to facilitate vision-based flying vehicle aircraft bracket detection research. 4361 surround view images collected from multiple scenes and lighting conditions to minimize the impact of complex visual environments on the detection performance. • The effectiveness of the proposed method is verified in our collected dataset (91.5% mAP and 35.79 FPS). The method is also applied to parking slot detection, demonstrating comparable detection performance to existing methods while significantly improving detection speed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".