中国舰船研究2026，Vol.21Issue(3)：64-75,12.DOI:10.19693/j.issn.1673-3185.04355
基于多维特征的舰载机舰面作业识别
Recognition of carrier flight deck operations based on multi-dimensional features
摘要
Abstract
[Objective]To address the challenges brought by unique flight operational scenarios and insuffi-cient public data for carrier flight deck operations,this study proposes a recognition method based on multi-dimensional features.[Methods]First,key points such as deck passage boundaries and static obstacles are accurately selected to represent the environmental information.Interactions between dynamic operational par-ticipants and static deck facilities are modelled using graph convolutional networks to explore their underlying connections of deck operation interaction relationships.Then,a multi-scale spatio-temporal feature extraction(MS-STFE)module is designed,incorporating a dilated attention mechanism that captures key individual in-teractions at both global and local levels by applying different dilation rates.At the same time,temporal con-volutional networks(TCN)combined with the attention mechanism are employed to extract temporal interac-tion features,efficiently capturing dynamic relationships across both long and short sequences.Finally,the MS-STFE module is stacked multiple times to adaptively extract multi-dimensional features,thereby improv-ing the recognition accuracy of carrier flight deck operations.[Results]Experiments conducted on a self-constructed dataset featuring multi-perspective carrier flight deck operation scenarios involving heterogeneous deck operation entities demonstrate that the proposed method significantly outperforms existing group activity recognition models such as ARG,DIN,AT,and GroupFormer,achieving an accuracy of 97.8%.[Conclusion]This study provides a valuable reference for the high-accuracy recognition of carrier flight deck operations.关键词
航空母舰/航母甲板作业/舰载机甲板保障作业/多维特征/时空特征/注意力机制Key words
aircraft carriers/carrier flight deck operations/carrier aircraft deck support operations/multi-dimensional features/spatio-temporal feature/attention mechanism分类
交通工程引用本文复制引用
郝天然,祝佳笑,李文婷,李超超,吕培,徐明亮..基于多维特征的舰载机舰面作业识别[J].中国舰船研究,2026,21(3):64-75,12.基金项目
国家重点研发计划项目(2021YFB3301504) （2021YFB3301504）
国家自然科学基金资助项目(62372415,62102371) （62372415,62102371）
国家自然科学基金重点项目(62036010) （62036010）
装备预研教育部联合基金资助项目(8091B032257) （8091B032257）
