LSCD-Pose: A Feature Point Detection Model for Collaborative Perception in Airports

Ensuring safety on busy airport aprons remains challenging, particularly in preventing aircraft wingtip collisions. In this study, first, a simplified coordinate mapping method converts pixel detections into accurate spatial coordinates, improving aircraft position and velocity estimates. Next, an innovative dynamic warning area with a classification mechanism is introduced to enable faster responses from airport staff. Finally, this study proposes LSCD-Pose, a real-time detection network enhanced by lightweight shared modules, significantly reducing model size and computational load without sacrificing accuracy. Experiments on real airport datasets representing various apron scenarios demonstrate frame rates up to 461.7 FPS and a 90.5% reduction in model size compared with the baseline. Visualizations confirm the solution's versatility and efficiency in effectively mitigating wingtip collisions and enhancing apron safety.