Differentiable Sparse Mask Guided Infrared Small Target Fast Detection Network

SHENG Weidong; WU Shuanglin; XIAO Chao; LONG Yunli; LI Xiaobin; ZHANG Yiming

doi:10.11999/JEIT250989

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SHENG Weidong, WU Shuanglin, XIAO Chao, LONG Yunli, LI Xiaobin, ZHANG Yiming. Differentiable Sparse Mask Guided Infrared Small Target Fast Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250989

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SHENG Weidong, WU Shuanglin, XIAO Chao, LONG Yunli, LI Xiaobin, ZHANG Yiming. Differentiable Sparse Mask Guided Infrared Small Target Fast Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250989

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Differentiable Sparse Mask Guided Infrared Small Target Fast Detection Network

doi: 10.11999/JEIT250989 cstr: 32379.14.JEIT250989

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

Received Date: 2025-09-24
Accepted Date: 2025-12-08
Rev Recd Date: 2025-12-08

Available Online: 2025-12-11

Abstract

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

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References(31)

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