Real-time aircraft bracket junction point detection for split flying vehicle module docking - Wang, Weida; Wan, Chenglin; Li, Ying; Yang, Chao; Deng, Zejian; Xu, Bin; Xiang, Changle (Green Energy and Intelligent Transportation, 2025)
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 4,631 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. [Display omitted] •A vision-based approach for flying vehicle aircraft bracket detection is proposed.•A junction point complementation scheme is designed for the aircraft bracket.•A dataset is created to facilitate vision-based flying vehicle aircraft bracket detection research.•The effectiveness of the proposed method is verified in our collected dataset (91.5% mAP and 35.79 FPS).•The lightweight network can meet the real-time requirement on edge computing platforms.