可微稀疏掩膜引导的红外小目标快速检测网络

盛卫东; 吴双林; 肖超; 龙云利; 李晓斌; 张一鸣

doi:10.11999/JEIT250989

可微稀疏掩膜引导的红外小目标快速检测网络

doi: 10.11999/JEIT250989 cstr: 32379.14.JEIT250989

盛卫东¹,
吴双林¹,
肖超^{1, 2, ,},
龙云利¹,
李晓斌²,
张一鸣²

1.
国防科技大学电子科学学院长沙 410073
2.
卫星信息智能处理与应用技术国家级重点实验室北京 10085

基金项目: 国家自然科学基金青年基金(C类)(项目号62501609)

详细信息

作者简介:
盛卫东：男，副研究员，研究方向为光学遥感图像弱小目标检测与跟踪技术

吴双林：女，博士，研究方向为红外小目标检测技术

肖超：男，助理研究员，研究方向为光学遥感图像弱小目标检测技术

龙云利：男，高工，研究方向为红外弱小目标检测技术

李晓斌：男，研究员，研究方向为卫星信息智能处理

张一鸣：男，副研究员，研究方向为卫星信息智能处理

通讯作者:
肖超　xiaochao12@nudt.edu.cn

中图分类号: TN911.73
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- 被引次数: 0
出版历程
- 收稿日期: 2025-09-24
- 修回日期: 2025-12-08
- 录用日期: 2025-12-08
- 网络出版日期: 2025-12-11

Differentiable Sparse Mask Guided Infrared Small Target Fast Detection Network

1.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
2.
Satellite Information Intelligent Processing and Application Research Laboratory, Beijing 100085, China

Funds: National Natural Science Foundation of China under Grant No. 62501609

摘要

摘要: 红外小目标检测在遥感探测、红外制导、环境监测等领域具有不可替代的应用价值，其核心挑战在于目标像素占比极小(目标尺寸通常小于9×9)、空间特征稀疏且易被复杂背景杂波淹没。现有红外小目标方法或依赖手工设计的背景抑制算子，难以适应复杂场景；或采用密集卷积神经网络，未充分考虑目标背景占比极不均衡导致的计算冗余。基于目标稀疏先验，本文提出一种可微稀疏掩膜引导的红外小目标快速检测网络。首先，设计可微稀疏掩膜生成模块作为预处理，输出目标候选区域的二值掩码，实现对目标的粗检测，并过滤大量背景冗余信息；其次，基于Minkowski Engine稀疏卷积构建稀疏特征提取模块，仅对二值掩码中的非零目标区域进行稀疏卷积运算，实现对目标候选区域的精细化处理；最后，通过金字塔池化模块进行多尺度特征融合，并将融合后的特征送入目标-背景二分类器输出最终检测结果。为验证方法有效性，在NUDT-SIRST与NUAA-SIRST两大主流红外小目标数据集上进行实验，实验结果表明，所提方法实现了在检测性能相当的情况下，实现了检测效率的极大改善，验证了所提方法的有效性。
- 红外小目标检测 /
- 可微稀疏掩膜 /
- 稀疏特征提取 /
- 金字塔池化
Abstract: Objective Infrared small target detection holds significant and irreplaceable application value across various critical domains, including infrared guidance, environmental monitoring, and security surveillance. Its importance is underscored by tasks such as early warning systems, precision targeting, and pollution tracking, where timely and accurate detection is paramount. The core challenges in this domain stem from the inherent characteristics of infrared small targets: their extremely small size (typically less than 9×9 pixels), limited spatial features due to long imaging distance and the high probability of being overwhelmed by complex and cluttered backgrounds, such as cloud cover, sea glint, or urban thermal noise. These factors make it difficult to distinguish genuine targets from background clutter using conventional methods. Existing approaches to infrared small target detection can be broadly categorized into traditional model-based methods and modern deep learning techniques. Traditional methods often rely on manually designed background suppression operators, such as morphological filters (e.g., Top-Hat) or low-rank matrix recovery (e.g., IPI). While these methods are interpretable in simple scenarios, they struggle to adapt to dynamic and complex real-world environments, leading to high false alarm rates and limited robustness. On the other hand, deep learning-based methods, particularly those employing dense convolutional neural networks (CNNs), have shown improved detection performance by leveraging data-driven feature learning. However, these networks often fail to fully account for the extreme imbalance between target and background pixels—where targets typically constitute less than 1% of the entire image. This imbalance results in significant computational redundancy, as the network processes vast background regions that contribute little to the detection task, thereby hampering efficiency and real-time performance. To address these challenges, exploiting the sparsity of infrared small targets offers a promising direction. By designing a sparse mask generation module that capitalizes on target sparsity, it becomes feasible to coarsely extract potential target regions while filtering out the majority of redundant background areas. This coarse target region can then be refined through subsequent processing stages to achieve satisfactory detection performance. This paper presents an intelligent solution that effectively balances high detection accuracy with computational efficiency, making it suitable for real-time applications. Methods This paper proposes an end-to-end infrared small target detection network guided by a differentiable sparse mask. First, an input infrared image is preprocessed with convolution to generate raw features. A differentiable sparse mask generation module then uses two convolution branches to produce a probability map and a threshold map, and outputs a binary mask via a differentiable binarization function to extract target candidate regions and filter background redundancy. Next, a target region sampling module converts dense raw features into sparse features based on the binary mask. A sparse feature extraction module with a U-shaped structure (composed of encoders, decoders, and skip connections) using Minkowski Engine sparse convolution performs refined processing only on non-zero target regions to reduce computation. Finally, a pyramid pooling module fuses multi-scale sparse features, and the fused features are fed into a target-background binary classifier to output detection results. Results and Discussions To fully validate the effectiveness of the proposed method, comprehensive experiments were conducted on two mainstream infrared small target datasets: NUAA-SIRST, which contains 427 real-world infrared images extracted from actual videos, and NUDT-SIRST, a large-scale synthetic dataset with 1327 diverse images. The method was compared against 3 representative traditional algorithms (e.g., Top-Hat, IPI) and 6 state-of-the-art deep learning methods (e.g., DNA-Net, ACM). Results demonstrate the method achieves competitive detection performance: on NUAA-SIRST, it attains 74.38% IoU, 100% Pd, and 7.98×10^-⁶ Fa; on NUDT-SIRST, it reaches 83.03% IoU, 97.67% Pd, and 9.81×10^-⁶ Fa, matching the performance of leading deep learning methods. Notably, it excels in efficiency: with only 0.35M parameters, 11.10G Flops, and 215.06 FPS, its FPS is 4.8 times that of DNA-Net, significantly cutting computational redundancy. Ablation experiments (Fig.6) confirm the differentiable sparse mask module effectively filters most backgrounds while preserving target regions. Visual results (Fig.5) show fewer false alarms than traditional methods like PSTNN, as its "coarse-to-fine" mode reduces background interference, verifying balanced performance and efficiency. Conclusions This paper addresses the massive computational redundancy of existing dense computing methods in infrared small target detection—caused by extremely unbalanced target-background proportion (target proportion is usually smaller than 1% of the whole image)—by proposing a fast infrared small target detection network guided by a differentiable sparse mask. The network adaptively extracts candidate target regions and filters background redundancy via a differentiable sparse mask generation module, and constructs a feature extraction module based on Minkowski Engine sparse convolution to reduce computation, forming an end-to-end "coarse-to-fine" detection framework. Experiments on NUDT-SIRST and NUAA-SIRST datasets demonstrate that the proposed method achieves comparable detection performance to existing deep learning methods while significantly optimizing computational efficiency, balancing detection accuracy and speed. It provides a new idea for reducing redundancy based on sparsity in infrared small target detection, is applicable to scenarios like remote sensing detection, infrared guidance and environmental monitoring that require both real-time performance and accuracy, and offers useful references for the lightweight development of the field.
- Infrared small target detection /
- Differentiable sparse mask /
- Sparse feature extraction /
- Pyramid pooling

HTML全文

图 1 红外小目标典型场景及真值展示图

下载: 全尺寸图片幻灯片

图 2 本文所提方法的网络总体结构图

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图 3 可微稀疏掩膜生成模块示意图

下载: 全尺寸图片幻灯片

图 4 稀疏特征提取模块示意图

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图 5 不同检测方法可视化结果展示图

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图 6 可微稀疏掩膜生成模块生成的掩膜展示图

下载: 全尺寸图片幻灯片

表 1 在NUAA-SIRST和NUDT-SIRST数据下不同方法性能对比

方法	NUAA-SIRST			NUDT-SIRST			#Params (M)	Flops (G)	FPS
方法	IoU (%)	Pd (%)	Fa (×10^–6)	IoU (%)	Pd (%)	Fa (×10^–6)	#Params (M)	Flops (G)	FPS
Top-Hat^[22]	7.14	79.84	1012	20.72	78.41	166.70	-	-	336.36
IPI^[4]	25.67	85.55	11.47	17.76	74.49	41.23	-	-	0.12
PSTNN^[6]	22.40	77.95	29.11	14.85	66.13	44.17	-	-	5.4
MDvsFA^[8]	60.30	89.35	56.35	74.14	90.47	25.34	3.92	264.96	4.72
ACM^[9]	70.33	93.91	3.73	67.08	95.97	10.18	0.52	0.43	180.32
ISTDU^[13]	58.83	89.91	40.63	78.80	97.04	21.51	2.76	7.44	134.28
DNA-Net^[7]	76.24	97.71	12.80	87.09	98.73	4.22	4.70	14.02	45.20
RDIAN^[14]	68.98	96.33	29.63	73.36	94.82	47.94	0.22	3.69	278.80
HoLoCoNet^[15]	73.89	100.00	19.87	80.90	97.67	13.54	0.70	6.60	125.49
本文方法	74.38	100.00	7.98	83.03	97.67	9.81	0.35	11.10	215.06

下载: 导出CSV

表 2 在NUAA-SIRST数据集上对金字塔池化模块有效性验证的结果

方法	IoU (%)	Pd (%)	Fa (×10^–6)	#Params (M)	Flops (G)	FPS
消融模型	67.7	98.17	30.10	0.34	8.68	252.05
本文方法	74.38	100.00	7.98	0.35	11.10	215.06

下载: 导出CSV

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