Tb Technical Research on Recognition of Typical Pilot Operations Based on Visual Image Sequences

With the rise of single-pilot operations, precise identification of pilot actions is vital for flight safety. Traditional sensors and existing vision-based technologies face limitations in real-time monitoring due to intrusiveness, environmental adaptability, dynamic interference, and occlusion This research focuses on pilot hand action and pose recognition based on visual image sequences, addressing the critical challenges of complex cockpit environments and low hand tracking accuracy. To enhance hand action recognition, a machine learning-based object detection and recognition technique is proposed for accurate tracking of hand dynamics and efficient posture monitoring. Through the construction of a “human-machine-ground” collaborative perception system, this study aims to deliver realtime decision support for intelligent monitoring within a singlepilot operational context, thereby fostering the evolution of aviation safety from a passive to a proactive paradigm. In terms of hand action recognition, this project propose a machine learningbased object detection and recognition technique to achieve accurate acquisition of hand dynamics and effective monitoring of body posture. This project employs machine learning algorithms to optimize pilot action recognition, emphasizing improved accuracy in detecting operational postures within complex environments. A temporal gesture image dataset is constructed based on cockpit video data, and a YOLO model is trained to recognize typical operational behaviors. The robustness of the algorithm in occlusion scenarios is particularly emphasized. Following iterative parameter optimization, the trained model demonstrates proficiency in recognizing typical pilot operational gestures, achieving mean average precision (mAP) scores of 0.85 and 0.72 for two distinct gesture classifications. However, limitations remain in distinguishing relevant gestures from extraneous objects, leading to occasional misjudgments.