红外技术2025，Vol.47Issue(6)：729-738,10.
红外小目标混频特征融合检测模型
Infrared Small Target Detection with Mixed-Frequency Feature Fusion Detection Model
摘要
Abstract
In infrared imaging,small targets often exhibit indistinct contours and sparse texture information,presenting a significant challenge for identification based solely on their inherent characteristics.To address this limitation,a novel mixed-frequency feature detection(MFFD)model is proposed.This model substantially improves small-object detection performance by leveraging both the contextual information of the target and its surrounding background.The MFFD model introduces a mixed-frequency extraction module that enhances small-target recognition by integrating global low-frequency semantic features with local high-frequency target details.Additionally,a multi-stage fusion module is employed to effectively coordinate feature interaction and integration across multiple levels,thereby improving semantic understanding and spatial information fusion.On the publicly available NUAA-SIRST and IRSTD-1k datasets,MFFD-Net outperformed five other deep learning-based methods.Compared to AGPC-Net,MFFD-Net achieved significant improvements in IoU and nIoU metrics.For the NUAA-SIRST dataset,increases of 4.42％ and 4.33％ were observed,respectively,while for the IRSTD-1k dataset,the corresponding improvements were 3.63％ and 6.38％.These results demonstrate the strong potential of the proposed model for detecting small objects in complex infrared backgrounds.关键词
红外图像/小目标检测/Transformer/深度学习/特征融合Key words
infrared imaging/small target detection/transformer/deep learning/feature fusion分类
计算机与自动化引用本文复制引用
李才荣,王志社,李晋红,任乃奎,王春发..红外小目标混频特征融合检测模型[J].红外技术,2025,47(6):729-738,10.基金项目
山西省基础研究计划资助项目(202203021221144、202203021211192、202303021212221) （202203021221144、202203021211192、202303021212221）
太原科技大学科研启动基金(20222122,20232036). （20222122,20232036）
