一种基于FD_Net网络识别模型的复杂飞行动作识别方法OA

Because of the problems that accuracy is low in recognizing complex flight action,and in order to enhance the accuracy and reliability of flight parameter data analysis,this paper proposes a flight ac-tion recognition method based on time convolutional network mapping anchor boxes.The method is to construct a FD̠Net recognition model by improving the YOLOv3 network structure,transforming the recognition of complex flight action into a problem of regional division and classification in the time di-mension.A selection method of complex flight action with key feature parameters is proposed,and an input system with 25 feature parameters is constructed.A data augmentation method based on scale scal-ing is adopted by solving the problem of sample imbalance.Loss functions for prediction box regression,confidence regression,and classification regression are designed to complete model training.The experi-mental results show that compared with the existing methods,the proposed method significantly im-proves the accuracy of complex flight action recognition and significantly enhances computational efficien-cy,and the effectiveness and practicality of the method are verified.