时空特征联合增强的空对空红外无人机检测

尹星宇; 金忍; 褚昭晨; 贾明东; 刘新福

doi:10.7527/S1000-6893.2026.33342

航空学报 >

0 1 - 0

DOI: https://doi.org/10.7527/S1000-6893.2026.33342

时空特征联合增强的空对空红外无人机检测

尹星宇 ,
金忍 ,
褚昭晨 ,
贾明东 ,
刘新福

展开

北京理工大学

收稿日期: 2026-01-07

修回日期: 2026-04-07

网络出版日期: 2026-04-14

基金资助

国家自然科学基金;国家重点研发计划 “社会治理与智慧社会科技支撑（平安中国）”重点专项“基于高分辨探测和智能低损反制的无人机防控关键技术与装备研发”项目

收起

Spatio-Temporal Feature Joint Enhancement for Air-to-Air Infrared UAV Small Target Detection

YIN Xing-Yu ,
JIN Ren ,
CHU Zhao-Chen ,
JIA Ming-Dong ,
LIU Xin-Fu

Expand

Received date: 2026-01-07

Revised date: 2026-04-07

Online published: 2026-04-14

Fold

摘要

基于视觉的空对空红外无人机检测是空中监视和蜂群对抗等领域的关键技术，本文针对红外视频序列中无人机目标边缘不清晰、在单一时刻下目标与背景对比度低的问题，提出一种时空特征联合增强的空对空红外无人机目标检测算法，在改进的通用U型网络结构中嵌入两类模块：其一，构建基于跨层级语义引导的空间注意力模块，在编码器阶段通过下采样生成的深层特征图语义信息对其上一级特征图对应区域的纹理特征进行加权锐化，得到空间增强特征图，实现对目标区域特征聚焦；其二，设计时空引导的多头注意力模块，设置时间窗口，在解码器阶段将时间窗口下经过解码的连续帧高维特征图与当前帧经过基于跨层级语义引导的空间注意力模块输出的空间增强特征图跨注意力运算，用多帧空间一致性特征增强当前目标表征，从而实现空间细节与时间上下文的联合特征增强。将算法在Drone-detection-dataset（无人机类子集）与Anti-UAV数据集上进行实验验证，结果表明，与当前主流红外小目标检测方法相比，相比性能较优的MFE-Net在mIoU指标上提升2.01%，虚警率降低0.72%；在Anti-UAV数据集上相比MFE-Net在mIoU指标上提升1.10%，虚警率降低1.21%，表明算法在红外图像空对空无人机检测精确性与稳定性方面均取得最优性能。

关键词： 无人机; 深度学习; 红外目标检测; 视频弱小目标检测; 时空特征; 跨层级语义引导

本文引用格式

尹星宇 , 金忍 , 褚昭晨 , 贾明东 , 刘新福 . 时空特征联合增强的空对空红外无人机检测[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2026.33342

Abstract

Vision-based air-to-air infrared unmanned aerial vehicle (UAV) detection is a key technology for aerial surveillance and swarm confrontation applications. Aiming at the challenges of blurred target boundaries and low target-to-background contrast at a single time instant in infrared video sequences, this paper proposes a spatio-temporal joint feature enhancement framework for air-to-air infrared UAV small target detection method. The proposed approach integrates two dedicated modules into an improved generic U-shaped network architecture. First, a Multi-Level Semantic-guided Spatial Attention Module is designed. High-level semantic information is extracted from down-sampled deep feature maps in the encoder and is used to adaptively enhance the corresponding textured regions of low-level feature maps, producing spatially enhanced features that focus on target regions. Second, a Spatial-Temporally Guided Multi-Head Attention Module is designed. By introducing a temporal window, the spatial-temporally guided multi-head attention module performs cross-attention. It takes the spatially enhanced features of the current frame from the multi-level semantic-guided spatial attention module as the query. It takes high-dimensional decoded features from multiple consecutive frames as the key and value. This design exploits spatial consistency across frames and reinforces the target representation. In this way, the targets’ spatial details and temporal contextual information are jointly enhanced. Extensive experiments are conducted on the Drone subset of the Drone-detection-dataset and the Anti-UAV dataset. The results demonstrate that, compared with state-of-the-art infrared small target detection methods, the proposed method achieves superior performance. Specifically, on the Drone-detection-dataset, our method improves the mIoU by 2.01% and reduces the false alarm rate by 0.72% compared with the strong baseline MFE-Net. On the Anti-UAV dataset, the proposed method achieves a 1.10% improvement in mIoU and a 1.21% reduction in false alarm rate over MFE-Net. These results indicate that the proposed method achieves improved detection accuracy and stability for air-to-air infrared UAV detection.

Key words： UAV; Deep Learning; Infrared Target Detection; Video-based Small Target Detection; Spatio-Temporal Features; Cross-Level Semantic Guidance

参考文献

[1]HUANG J, HE W, YANG J, et al.Quantity monitor based on differential weighing sensors for storage tank of agricultural uav[J].Drones, 2024, 8(3):92- [2]LI L, WANG Z C, ZHU J Q, et al.Smartphone-based task scheduling in uav networks for disaster relief[J].Electronics, 2024, 13(15):2903- [3]ALEXAN W, ALY L, KORAYEM Y, et al.Secure communication of military reconnaissance images over uav-assisted relay networks[J].IEEE Access, 2024, 12(-):78589-78610 [4]袁翔, 程塨, 李戈, 等.遥感影像小目标检测研究进展[J].中国图象图形学报, 2023, 28(6):1662-1684 [5]YUAN X, CHENG G, LI G, et al.Progress in small object detection for remote sensing images[J].Journal of Image and Graphics, 2023, 28(6):1662-1684 [6]HOU Q Y, ZHANG L W, TAN F J, et al.ISTDU-Net: Infrared small-target detection U-Net[J].IEEE Geoscience and Remote Sensing Letters, 2022, 19(-):7506205-- [7]ZHANG T F, LI L, CAO S Y, et al.Attention-guided pyramid context networks for detecting infrared small target under complex background[J].IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(4):4250-4261 [8]CHEN F, GAO C Q, LIU F C, et al.Local patch network with global attention for infrared small target detection[J].IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(5):3979-3991 [9]李峻宇, 刘乾坤, 付莹.融合注意力机制的红外小目标检测[J].航空学报, 2024, 45(14):628959- [10]LI J Y, LIU Q K, FU Y.Infrared small object detection based on attention mechanism[J].Acta Aeronautica et Astronautica Sinica, 2024, 45(14):628959- [11]LIU X Y, ZHU W M, YAN P, et al.IR-MPE: A long-term optical flow-based motion pattern extractor for infrared small dim targets[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 5005415. [12]SUN Y, YANG J G, AN W.Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model[J].IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5):3737-3752 [13]YAN P T, HOU R Z, DUAN X G, et al.STDMANet: Spatio-temporal differential mul-tiscale attention network for small moving infra-red target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5602516. [14]CHENG G, YUAN X, YAO X W, et al.Towards large-scale small object detection： survey and benchmarks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(11):13467-13488 [15]XU H, ZHONG S, ZHANG T X, et al.Multiscale multilevel residual feature fusion for real-time infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5002116. [16]YANG G Y, LEI J, ZHU Z K, et al.AFPN: Asymptotic feature pyramid network for object detection[C]// 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Honolulu, HI, USA: IEEE, 2023: 2184-2189. [17]LIU Q K, LIU R, ZHENG B L, et al.Infrared small target detection with scale and location sensitivity[C]// 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA: IEEE, 2024: 17490-17499. [18]HE A, LI X B, WU X M, et al.ALSS-YOLO: An adaptive lightweight channel split and shuffling network for tir wildlife detection in uav imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 17308-17326. [19]WANG K W, DU S Y, LIU C X, et al.Interior attention-aware network for infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing Letters, 2022, 60: 5002013. [20]DENG L Z, ZHU H, TAO C, et al.Infrared moving point target detection based on spatial-temporal local contrast filter[J]. Infrared Physics & Technology, 2016, 76: 168-173. [21]ZHU H, GUAN Y S, DENG L Z, et al.Infrared moving point target detection based on an anisotropic spatial-temporal fourth-order diffusion filter[J]. Computers & Electrical Engineering, 2018, 68: 550-556. [22]SUN Y, YANG J G, AN W.Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model[J].IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5):3737-3752 [23]WU F Y, YU H, LIU A R, et al.Infrared small target detection using spatio-temporal 4-D tensor train and ring unfolding[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5002922. [24]LI J, ZHANG P, ZHANG L Y, et al.Sparse regularization-based spatial–temporal twist tensor model for infrared small tar-get detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61:5000417. [25]LI R J, AN W, XIAO C, et al.Direction-Coded temporal U-shape module for multiframe infrared small target detection[J].IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(1):555-568 [26]CHEN S J, JI L P, ZHU J W, et al.SSTNet: Sliced spatio-temporal network with cross-slice ConvLSTM for moving infrared dim-small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5000912. [27]DUAN W W, JI L P, CHEN S J, et al.Triple-Domain feature learning with frequency-aware memory enhancement for moving infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5006014. [28]YING X Y, LIU L, LIN Z P, et al.Infrared small target detection in satellite videos: a new dataset and a novel recurrent feature refinement framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5002818. [29]SU Y Q, CHEN X, CANG C, et al.A space target detection method based on spatial-temporal local registration in complicated backgrounds[J]. Remote Sensing. 2024, 16(4):669. [30]WU F Y, YU H, LIU A R, et al.Infrared small target detection using spatio-temporal 4-D tensor train and ring unfolding[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5002922. [31]RONNEBERGER O, FISCHER P, BROX T.U-Net: Convolutional networks for biomedical image segmentation[C]// Medical Image Computing and Computer-Assisted Intervention(MICCAI). Cham: Springer, 2015: 234–241. [32]LI B Y, XIAO C, WANG L G, et al.Dense nested attention network for infrared small target detection[J]. IEEE Transactions on Image Processing, 2023, 32: 1745-1758. [33]JIANG N, WANG K R, PENG X K, et al. Anti-UAV: A large multi-modal benchmark for UAV tracking[DB/OL]. arXiv prep.[J].rXiv: 2101.08466, 2021., rint, :- [34]SVANSTR?M F, ALONSO-FERNANDEZ F, ENGLUND C.A dataset for multi-sensor drone detection[J]. Data in Brief, 2021, 39:107521. [35]WANG H, ZHOU L P, WANG L.Miss detection vs. false alarm: adversarial learning for small object segmentation in infrared images[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 2019: 8509-8518. [36]DAI Y M, WU Y Q, ZHOU F, et al.Attentional local contrast networks for infrared small target detection[J].IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11):9813-9824 [37]ZHANG M J, ZHANG R, YANG Y X, et al.ISNet: Shape matters for infrared small target detection[C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2022: 877-886. [38]WU X, HONG D F, CHANUSSOT J.UIU-Net: U-Net in U-Net for infrared small ob-ject detection[J]. IEEE Transactions on Image Processing, 2023, 32: 364-376. [39]DU J M, LU H Z, ZHANG L P, et al.A spatial-temporal feature-based detection framework for infrared dim small target[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 3000412.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献