电讯技术2025，Vol.65Issue(10)：1579-1586,8.DOI:10.20079/j.issn.1001-893x.250408001
基于改进SURF的飞行训练科目自动识别
Automatic Recognition of Flight Training Scenarios Based on an Optimized SURF Algorithm
摘要
Abstract
The existing flight training subjects identification methods exhibit latency in recognizing complex maneuvering actions,often overlooking the potential of modern digital technologies and the latest regulatory updates.The core challenge lies on reducing matching error rates while enhancing computational efficiency.To address these issues,a SURF-FLANN-RANSAC hybrid algorithmic framework(SFR)is proposed.First,an improved Speeded Up Robust Features(SURF)algorithm is utilized to extract and match image features within cockpit environments.To further enhance the efficiency and accuracy of feature matching,the Fast Library for Approximate Nearest Neighbors(FLANN)matcher is integrated into the process.Additionally,the Random Sample Consensus(RANSAC)algorithm is applied to eliminate mismatches,thereby improving the overall robustness of the system.Experimental results on a self-built dataset comprising three typical flight training scenarios,steeply banked turns,lazy eights,and chandelles,demonstrate that the proposed algorithm achieves recognition accuracies of 94.58%,62.95%,and 86.72%,respectively.Compared with that of the second-best performing algorithm,these results represent improvements of 1%,20%,and 4%,respectively,along with a significant enhancement in processing speed,offering strong technical support for the intelligent management of flight training.关键词
飞行训练动作识别/图像特征提取/特征点匹配/加速稳健特征(SURF)Key words
identification of flight training action/image feature extraction/feature point matching/speeded up robust features(SURF)分类
信息技术与安全科学引用本文复制引用
张娅岚,刘芳,刘伟杰,魏永超..基于改进SURF的飞行训练科目自动识别[J].电讯技术,2025,65(10):1579-1586,8.基金项目
中央高校基本科研业务费专项资金资助(24CAFUC03039) （24CAFUC03039）
西藏自治区科技重大专项资金资助(XZ202101ZY0017G) （XZ202101ZY0017G）
