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The boarding bridge is the active passageway connecting the terminal with the aircraft at the airport. The intelligentization of the docking system between the boarding bridge and the passenger cabin door becomes particularly important. For the passenger cabin door recognition and positioning system based on computer vision, the key component is the target detection system. The traditional target detection algorithm learns by extracting traditional manual features, which cannot meet the detection requirements of good robustness, fast speed and high accuracy. Based on the application of transfer learning in deep learning, using the SSD (Single Shot Multibox Detector) algorithm, using lightweight MobileNet as a feature extraction network, the target detection model with good robustness and high accuracy is achieved, and the passenger cabin door The identification and positioning of the model are robust to different styles of doors, partial occlusion, background changes, lighting changes, and motion blur, and can accurately complete the recognition function and solve the relative position of the doors in the current visual image.