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一种农机轨迹行为模式识别的时空交互掩码并联网络

张鑫雨 翟卫欣

计算机工程与应用2025,Vol.61Issue(21):167-181,15.
计算机工程与应用2025,Vol.61Issue(21):167-181,15.DOI:10.3778/j.issn.1002-8331.2506-0360

一种农机轨迹行为模式识别的时空交互掩码并联网络

Spatial-Temporal Interaction Masked Net for Agricultural Machinery Trajectory Operation Mode Identification

张鑫雨 1翟卫欣2

作者信息

  • 1. 中国农业大学,北京 100083
  • 2. 中国农业大学,北京 100083||农业农村部 农机作业监测与大数据应用重点实验室,北京 100083
  • 折叠

摘要

Abstract

Agricultural machinery trajectory operation mode identification refers to the process of identifying the activity scenarios of agricultural machinery by analyzing the spatial structure,boundary characteristics and other information of agricultural machinery trajectories,and assigning corresponding semantic labels to each unknown trajectory point.Aiming at the problems of insufficient mining of spatiotemporal dependency of agricultural machinery trajectories and noise inter-ference,this paper proposes a spatial-temporal interaction masked net(ST-MaskNet)for agricultural machinery trajectory operation mode identification.This method designs two modules:temporal patch transformer(TPT)and spatial cross aggregator(SCA).The TPT module is used to explore the interactive relationships between internal features of agricultural machinery trajectories at different time nodes and further enhances the model's contextual understanding of trajectory features through a synchronization prototype(SynProto)strategy.The SCA module captures spatial correlations between multiple trajectory points by aggregating features of neighboring nodes.Furthermore,to improve the generalization abili-tyof the model,a binary masked learning(BML)strategy is designed,introducing a reconstruction branch to pre-train the model to learn a more general trajectory feature representation.To validate the superiority of the proposed model,experi-ments are conducted on two real agricultural machinery trajectory datasets provided by the key laboratory of agricultural big data,ministry of agriculture and rural affairs.The experimental results show that ST-MaskNet achieves 90.54%and 94.17%accuracy on the rice harvester and tractor trajectory datasets,respectively,and its F1 score increased by 5.15,6.84 percentage points,respectively,compared with the second-best model.

关键词

农机轨迹行为模式识别/时空交互掩码网络/双分支掩码学习/时序分块Transformer/空间交叉聚合器

Key words

agricultural machinery trajectory operation mode identification/spatial-temporal interaction masked net/binary masked learning/temporal patch Transformer/spatial cross aggregator

分类

信息技术与安全科学

引用本文复制引用

张鑫雨,翟卫欣..一种农机轨迹行为模式识别的时空交互掩码并联网络[J].计算机工程与应用,2025,61(21):167-181,15.

基金项目

国家自然科学基金(32301691) (32301691)

国家重点研发计划(2025YFE0103600,2021YFB3901300) (2025YFE0103600,2021YFB3901300)

中国农业大学"2115人才培育发展支持计划". ()

计算机工程与应用

OA北大核心

1002-8331

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