北京航空航天大学学报2026，Vol.52Issue(4)：1269-1278,10.DOI:10.13700/j.bh.1001-5965.2024.0048
基于双域和全局上下文特征提取的红外小目标检测
Infrared small target detection based on dual-domain and global context feature extraction
摘要
Abstract
Aiming at two inherent problems in single frame infrared small target detection(ISTD):The small target lacks local information such as color,texture and shape;The small targets are readily lost during the continuous down-sampling process that yields high-level semantic information and the global receptive field.A double-domain and global context feature extraction network(DDGC-FENet)that is both precise and quick is suggested.The model includes a dual-domain feature extraction(DDFE)module and a global context feature extraction(GCFE)module.The DDFE module simultaneously learns the local contrast information of the small target and the background in the spatial domain and the frequency domain,so as to separate the target from the background.The GCFE module can globally model the feature map after multiple down-sampling to extract the global context and prevent the loss of target features in the deep layer of the network.Furthermore,the model fuses low-level and high-level features from both row and column directions using a two-way attention fusion(TWAF)module.The suggested approach outperforms cutting-edge techniques like AGPCNet,DNANet,and ISNet in terms of mIoU,nIoU,and F1,according to experiments conducted on a number of public datasets.关键词
小目标检测/红外图像/中心差分卷积/快速傅里叶卷积/深度学习Key words
small target detection/infrared image/central differential convolution/fast Fourier convolution/deep learning分类
信息技术与安全科学引用本文复制引用
任勇,朵琳,许渤雨,杨新..基于双域和全局上下文特征提取的红外小目标检测[J].北京航空航天大学学报,2026,52(4):1269-1278,10.基金项目
云南省科技厅重大科技专项计划(202302AD080006) Major Science and Technology Special Program of Yunnan Provincial Science and Technology Department(202302AD080006) （202302AD080006）
