Real-time aircraft bracket junction point detection for split flying vehicle module docking
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle