一种坐标通道注意力深度学习网络的军用飞机识别方法
A Method for Military Aircraft Recognition Using a Coordinate Attention-based Deep Learning Network
杨环宇 1王军 1吴祥 1薄煜明 1马立丰 1陆金磊1
作者信息
- 1. 南京理工大学自动化学院,江苏南京 210094
- 折叠
摘要
战场态势瞬息万变,利用可见光图像对敌方用于军事行动的飞机类型进行有效区分,对提供军事作战信息具有重要意义.针对现有军用飞机识别方法存在小目标飞机和环境背景复杂导致的模型特征提取困难、数据样本数量不足导致的模型训练不充分的问题,提出一种坐标通道注意力(ConvNeXt-Coordinate Attention,ConvNeXt-CA)深度学习网络军用飞机目标识别方法.该方法在ConvNeXt网络可以保留小目标飞机特征的基础上,引入CA机制设计CA-Stage模块,提升网络对于背景和前景的区分能力;采用数据增强的方式扩充数据集,以及使用迁移学习的策略提高模型的泛化能力,训练得到具备最优超参数的ConvNeXt-CA网络.实验结果表明,与传统的军用飞机识别方法和其他深度学习模型相比,基于迁移学习的ConvNeXt-CA网络在预测准确率上有明显的提升,且具备较强的泛化能力.
Abstract
The military combat information can be provided by using the visible images to effectively distinguish the types of enemy aircrafts used for military operations in a rapidly changing battlefield environment.To address the challenges associated with extracting the model features from small aircraft targets and complex environmental backgrounds,as well as the limited training data available in existing military aircraft recognition methods,this paper proposes a ConvNeXt with coordinate attention(ConvNeXt-CA)-based recognition method for military aircraft targets.Based on the fact that the ConvNeXt-CA network can retain the characteristics of small aircrafttargets,the proposed method introduces the CA mechanism to design a CA-Stage module,which improves the ability of the network to distinguish between background and foreground.It uses data augmentation to expand the data set and the migration learning strategy to improve the generalization capability of the model,and trains the ConvNeXt-CA network with optimal hyperparameters.The experimental results show that,compared with the traditional military aircraft identification methods and other deep learning models,the migration learning-based ConvNeXt-CA network has a significantly improved prediction accuracy and a strong generalization capability.
关键词
军用飞机识别/深度卷积神经网络/坐标注意力机制/迁移学习Key words
military aircraft recognition/deep convolutional neural network/coordinate attention mechanism/migration learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据