Research for the Detection of Aircraft Target in Real‐Time Based on YOLOv5

The rapid growth in civil aviation traffic and airport infrastructure has strained airport facilities, affecting safety and security. It is evident from the frequent displacement of aircraft. To ensure efficient airport operations and safety, developing and applying scientific and technological solutions that minimize accidents and improve space utilization and operational efficiency is crucial. This paper introduces an innovative real‐time aircraft detection model based on YOLOv5, tailored for addressing aircraft scraping on airfields. We developed this model by collecting extensive operational imagery to create a comprehensive dataset and using it to build a detection system within the YOLOv5 framework. The model was tested through simulated operations on a physical airfield model, achieving an impressive average detection accuracy of 0.912. These results confirm the model’s effectiveness in real‐world scenarios, significantly advancing precise aircraft detection.