The automatic docking of airport special vehicles is an inevitable requirement for the development of smart airports in the future; The key to achieving automatic docking is to accurately identify and position the aircraft door.Aiming at this problem,proposes a door recognition and position method based on improved YOLOv5 and monocular vision. By adding a lightweight convolutional block attention module (CBAM) to the model, the algorithm improves its ability to extract features from aircraft doors; To solve the problem of repetitive feature extraction in YOLOv5, a spatial pyramid pooling cross stage partial connection (SPPCSPC) is introduced, and the number of group convolution groups is improved to 4, improving the detection accuracy of the algorithm; By obtaining the pixels of corner points in the candidate frame and utilizing spatial geometric relationships, accurate three-dimensional positioning of the aircraft door is achieved. The experimental results show that the improved YOLOv5 algorithm mAP reaches 96.5%, which is 5.6% higher than the original algorithm. At 19 m and 1 m in front of the aircraft door, the real-time maximum positioning error is 0.15 m and 0.01 m, respectively, which can meet the requirements of maintaining a safe distance of 5-10 cm from the aircraft door after the completion of docking of special vehicles.