The safety of the apron is crucial for aviation operations, and wingtip scraping incidents are a common occurrence in this area. However, existing airport apron conflict detection models have shortcomings such as low accuracy and poor real-time performance. This paper proposes a apron aircraft conflict detection model based on keypoint detection, which detects potential wingtip accidents by fusing pixel and real-world coordinate conversion methods and the improved YOLOLv8-Pose algorithm. Results on the wingtip conflict detection dataset indicate that our model, compared to the original YOLOv8-Pose, achieved an 87.3% increase in FPS, a 96.76% reduction in the number of parameters, and an 82.14% decrease in computational complexity.