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

Volume 47 Issue 12

Dec. 2026

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(12): 4779-4789

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, 2025, 47(12): 4779-4789. 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, 2025, 47(12): 4779-4789. 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: The National Natural Science Foundation of China (62501609)

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

Available Online: 2025-12-11

Publish Date: 2025-12-10

Abstract

Abstract

Objective Infrared small target detection has significant and irreplaceable application value in infrared guidance, environmental monitoring, and security surveillance. Its relevance is reflected in early warning, precision targeting, and pollution tracking, where timely and accurate detection is required. Core challenges arise from the inherent properties of infrared small targets: extremely small size (typically less than 9 × 9 pixels), limited spatial features due to long imaging distance, and a high likelihood of being submerged in complex and cluttered backgrounds such as clouds, sea glint, or urban thermal noise. These factors hinder reliable separation of true targets from background clutter using conventional approaches. Existing methods are generally divided into traditional model-based techniques and modern deep learning techniques. Traditional methods rely on manually designed background suppression operators, such as morphological filters (e.g., Top-Hat) or low-rank matrix recovery (e.g., IPI). Although interpretable in simple scenes, they adapt poorly to dynamic and complex environments, leading to high false alarm rates and limited robustness. Deep learning methods, particularly dense Convolutional Neural Networks (CNNs), achieve improved performance through data-driven feature learning. However, they do not sufficiently address the extreme imbalance between target and background pixels, with targets usually accounting for less than 1% of an image. Therefore, substantial computational redundancy occurs because large background regions contribute little to detection, which limits efficiency and real-time capability. Exploiting the sparsity of infrared small targets therefore provides a practical direction. By introducing a sparse mask generation module that uses target sparsity, potential target regions can be coarsely extracted while most redundant background areas are suppressed, followed by refinement in later stages. This study presents a solution that balances detection accuracy and computational efficiency for real-time applications. Methods An end-to-end infrared small target detection network guided by a differentiable sparse mask is proposed. First, an input infrared image is first processed by convolution to obtain raw features. A differentiable sparse mask generation module then adopts two convolution branches to generate a probability map and a threshold map, and produces a binary mask through a differentiable binarization function to extract candidate target regions and suppress background redundancy. Next, a target region sampling module converts dense raw features into sparse features according to the binary mask. A sparse feature extraction module with a U-shaped structure, composed of encoders, decoders, and skip connections, applies Minkowski Engine sparse convolution to perform refined processing only on non-zero target regions, thereby reducing computation. Finally, a pyramid pooling module fuses multi-scale sparse features, which are fed into a target-background binary classifier to generate detection results. Results and Discussions Comprehensive experiments are conducted on two mainstream infrared small target datasets: NUAA-SIRST, which includes 427 real infrared images extracted from real videos, and NUDT-SIRST, a large-scale synthetic dataset containing 1 327 diverse images. Comparisons are made with three representative traditional algorithms (e.g., Top-Hat, IPI) and six state-of-the-art deep learning methods (e.g., DNA-Net, ACM). The proposed method achieves competitive detection performance. On NUAA-SIRST, it attains 74.38% IoU, 100% P_d, and 7.98 × 10^–6 F_a. On NUDT-SIRST, it reaches 83.03% IoU, 97.67% P_d, and 9.81 × 10^–6 F_a, which is comparable to leading deep learning approaches. High efficiency is observed, with only 0.35 M parameters, 11.10 GFLOPs, and 215.06 fps. The frame rate is 4.8 times that of DNA-Net, indicating a substantial reduction in computational redundancy. Ablation experiments (Fig. 6) confirm that the differentiable sparse mask module effectively suppresses most background regions while retaining target areas. Visual results (Fig. 5) show fewer false alarms than traditional methods such as PSTNN, as the coarse-to-fine strategy reduces background interference and supports a balance between accuracy and efficiency. Conclusions A fast infrared small target detection network guided by a differentiable sparse mask is proposed to address the severe computational redundancy of dense computation methods, which originates from the extreme imbalance between target and background pixels (target proportion is usually smaller than 1% of the whole image). Candidate target regions are adaptively extracted and background redundancy is filtered through a differentiable sparse mask generation module. A sparse feature extraction module based on Minkowski Engine sparse convolution further reduces computation, forming an end-to-end coarse-to-fine detection framework. Experiments on the NUAA-SIRST and NUDT-SIRST datasets show that the proposed method achieves detection performance comparable to existing deep learning methods while significantly optimizing computational efficiency. The method supports real-time requirements in scenarios such as remote sensing detection, infrared guidance, and environmental monitoring, and provides a practical reference for lightweight development in infrared small target detection.
- Infrared small target detection,
- Differentiable sparse mask,
- Sparse feature extraction,
- Pyramid pooling

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References(31)

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