A Complex Flight Action Recognition Method Based On the FD_Net Network Recognition Model
MA Jinlong
LI Zhengxin
SHI Meilin
SHAN Shengzhe
DENG Tao
WU Shihui
Abstract：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.
Keywords：flight action recognitiondeep learningflight action annotationdata augmentation
Publication Date：2026-02-28
Online Publishing Date：2026-03-13(First online date of this platform, not the publication date of the document)
Pages：11( 1-11 )