FM-detector: End-to-end flight maneuver recognition method based on flight data

Abstract

Flight maneuver recognition (FMR) refers to automatic recognizing a series of aircraft flight patterns and is a key technology for flight training evaluation. However, the traditional FMR methods generally have higher computational complexity and lower recognition accuracy as these methods cannot effectively combine data segmentation, feature extraction and classification. We integrate all phases of FMR into a deep learning framework called FM-detector, instead of designing data segmentation, feature extraction, and classification methods separately. FM-detector consists of three parts: backbone for feature extraction, RPN for generating boundary proposals and CRN for flight maneuver classification and location. The experiment showed that our FM-detector achieved over 97% mAP in recognizing flight maneuvers, with higher quality predicted boundary and lower inference time. Code is available at https://gitcode.net/m0_46456645/fm-detector.