多维融合注意力增强的红外小目标检测方法

李卫星; 王帅; 陈怀宇; 盛卫东

doi:10.11999/JEIT260040

多维融合注意力增强的红外小目标检测方法

doi: 10.11999/JEIT260040 cstr: 32379.14.JEIT260040

1.
国防科技大学电子科学学院长沙 410073
2.
中国科学院西安光学精密机械研究所西安 710061

基金项目: 国家自然科学基金(62401589, 62403476)

详细信息

作者简介:
李卫星：男，副教授，研究方向为红外图像处理及轻量化，weixingli@nudt.edu.cn

王帅：男，副教授，研究方向为边缘计算

陈怀宇：男，助理研究员，研究方向为红外图像处理

盛卫东：男，副研究员，研究方向为光电探测

通讯作者:
盛卫东　shengweidong111@sohu.com

中图分类号: TN216
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- 被引次数: 0
出版历程
- 收稿日期: 2026-01-13
- 修回日期: 2026-06-27
- 录用日期: 2026-07-06
- 网络出版日期: 2026-07-19

Infrared Small Target Detection Enhanced by Multi-Dimensional Fusion Attention

1.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
2.
Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710061, China

Funds: The National Natural Science Foundation of China (62401589, 62403476)

摘要

摘要: 针对红外小目标在深层卷积神经网络中特征弥散、易受复杂背景干扰而导致检测性能下降的问题，该文提出一种基于多维融合注意力机制的红外小目标检测方法。设计通道-空间注意力特征增强模块，通过并行分支结构聚合跨维度显著性特征，抑制目标特征在深层网络的弥散趋势；通过自适应跨维度信息交互，建立通道-空间的跨域依赖关系，增强微弱目标在深层网络的特征响应。该方法仅需极低计算复杂度即可灵活嵌入基线模型，实现目标深度特征聚拢。在NUDT-SIRST数据集上的实验表明，所提方法在高杂波、低信噪比场景下表现出更强的鲁棒性和泛化能力。基于自研的轻量化智能处理单元进行边缘部署性能验证，实测单帧推理时延为46.7 ms，满足工程应用中实时性与高精度要求。
- 红外小目标 /
- 注意力机制 /
- 特征融合 /
- 边缘推理
Abstract: Objective Infrared imaging offers advantages including long operating range, wide coverage, high concealment, and all-weather operation, making it suitable for aerospace surveillance, maritime emergency rescue, forest fire monitoring, and remote sensing. Infrared sensors mounted on satellites and aircraft typically acquire long-range images in which targets occupy only a few pixels and lack discriminative texture and shape features. Moreover, weak thermal radiation signals are easily overwhelmed by background clutter. Existing infrared small target detection networks face two major challenges. First, repeated downsampling used to enlarge the receptive field causes small-target features to disappear in deep networks. Second, small-target features become increasingly diffused during deep feature extraction. Therefore, achieving accurate and reliable infrared small target detection under long-range imaging and complex background conditions remains an active research topic. Methods A Multi-Dimensional Fusion Attention Module (MFAM) based on channel-spatial attention is proposed to address feature diffusion caused by the small size of infrared targets. The proposed module captures feature dependencies across the channel, height, and width dimensions. Feature fusion and cross-dimensional interaction are jointly exploited to suppress the diffusion of small-target features in deep networks and strengthen the representation of weak infrared targets. Channel attention and spatial attention are applied in parallel directly to the input feature map, enabling feature extraction and information interaction in the channel and spatial domains, respectively. In the channel domain, compressed features are processed using a Multi-Layer Perceptron (MLP) to model inter-channel dependencies. In the spatial domain, Global Average Pooling (GAP) and Global Maximum Pooling (GMP) encode spatial information along the height and width dimensions. The outputs of the two branches are fused through a Sigmoid activation function to generate the final attention map. Unlike conventional hybrid attention mechanisms that connect channel attention and spatial attention sequentially, the proposed parallel architecture performs refined encoding of the channel, height, and width dimensions directly from the original input feature map. This design preserves both shallow spatial details and deep contextual semantics while reducing information loss during feature propagation. Owing to its plug-and-play design, MFAM can be seamlessly integrated into backbone networks such as ResNet and DNA-Net without introducing complex additional structures, demonstrating excellent compatibility. Results and Discussions The proposed method is evaluated on the publicly available NUDT-SIRST dataset. After MFAM is integrated into the baseline DNA-Net, the Intersection over Union (IoU), detection rate (Pd), and false alarm rate (Fa) reach 87.34%, 98.72%, and 3.22×10^–6, respectively. Compared with CBAM, BAM, GAM, CA, and TA, MFAM improves IoU by 0.40%, 1.68%, 2.46%, 2.11%, and 1.61%, respectively (Table 1). CBAM applies spatial attention after channel attention, which increases the risk of information loss during feature propagation. CA and BAM rely solely on GAP for feature encoding and therefore fail to exploit the complementary information provided by GMP. TA models cross-dimensional dependencies through rotation operations but cannot achieve simultaneous interaction among the channel, height, and width dimensions. By jointly integrating channel and spatial attention, MFAM enables effective cross-domain information interaction. The combined use of GAP and GMP further strengthens contextual and local feature representation, resulting in more accurate infrared small target detection. MFAM is also incorporated into ALCNet and AMFU-Net, improving IoU by 0.43% and 0.28%, respectively, compared with CBAM (Table 2). Ablation experiments further demonstrate the effectiveness of the proposed design. Compared with the serial attention architecture, the parallel fusion strategy improves IoU and Pd by 0.37% and 0.39%, respectively, while reducing Fa by 1.41×10^–6. These improvements result from applying channel attention and spatial attention directly to the original input feature map, thereby alleviating the diffusion of small-target features in deep networks. To verify inference performance on edge devices, the proposed method is deployed on an FPGA-GPU heterogeneous platform based on an FPGA and an NVIDIA Jetson AGX Xavier. Experimental results demonstrate successful edge deployment, with an average inference latency of 46.7 ms per 256×256 image (Fig. 8), satisfying real-time processing requirements. Conclusions A MFAM is proposed for infrared small target detection. The module effectively aggregates channel and spatial salient features while enabling adaptive cross-dimensional interaction, thereby improving the preservation of small-target features in deep networks and enhancing detection robustness. Owing to its lightweight plug-and-play design, MFAM can be readily integrated into existing detection networks. Experimental results demonstrate superior performance over existing methods in terms of IoU, P_d, and F_a. Furthermore, a lightweight intelligent processing unit based on an FPGA-GPU heterogeneous platform is developed to enable real-time deployment of the proposed algorithm on edge devices, demonstrating its practicality for engineering applications.
- Infrared small target /
- Attention mechanism /
- Feature fusion /
- Edge inference

HTML全文

图 1 基于注意力机制的红外小目标检测总体架构

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图 2 通道-空间注意力机制

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图 3 基于多维融合注意力机制的网络结构

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图 4 MFAM计算流程

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图 5 不同注意力机制的特征图可视化对比

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图 6 不同注意力机制的红外小目标检测二维可视化图

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图 7 自研FPGA和GPU处理模块

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图 8 边缘侧推理结果及时间

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表 1 不同算法目标检测性能(NUDT-SIRST)

类型	算法	IoU(%)	P_d(%)	Fa(×1e–6)
传统算法	Top-Hat	20.70	78.41	166.70
	IPI	17.76	74.49	41.23
	RIPT	29.44	91.85	344.30
深度学习算法	DNA-Net(基线)	85.39	97.67	9.49
	+SE	86.20	98.64	3.98
	+SK	85.30	98.41	12.80
	+ECA	85.50	98.41	6.78
	+CBAM	86.94	98.20	3.24
	+BAM	85.66	98.20	5.79
	+GAM	84.88	98.41	33.96
	+CA	85.25	97.57	9.77
	+TA	85.75	98.31	12.82
	+MFAM(本文)	87.34	98.72	3.22

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表 2 注意力模块嵌入不同基线模型的性能

算法	IoU(%)	P_d(%)	F_a(×10^–6)
ALC-Net (基线)	71.90	97.14	29.00
+CBAM	72.15	95.24	4.16
+MFAM (本文)	72.58	97.46	8.92
AMFU-Net (基线)	81.17	97.04	32.24
+CBAM	83.04	98.41	15.49
+MFAM (本文)	83.32	97.35	11.40

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表 3 不同注意力融合方式的性能对比

算法	IoU(%)	P_d(%)	Fa(×1e–6)
基线模型	85.39	97.67	9.49
+C	86.78	98.18	8.95
+S	85.27	97.43	13.24
+CS，串联	86.97	98.33	4.63
+CS，并联(本文)	87.34	98.72	3.22

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表 4 不同信息编码方式的性能对比

算法	IoU(%)	Pd(%)	F_a(×10^–6)
基线模型	85.39	97.67	9.49
GAP	85.86	98.12	9.43
GMP	86.98	98.43	6.75
GAP+GMP(本文)	87.34	98.72	3.22

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