Dynamic Frequency Guidance and Semantic Purification Network for Infrared Dim and Small Target Detection

SHEN Xiaoru; CHANG Xia; WEI Wenjie

doi:10.11999/JEIT260582

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SHEN Xiaoru, CHANG Xia, WEI Wenjie. Dynamic Frequency Guidance and Semantic Purification Network for Infrared Dim and Small Target Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260582

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SHEN Xiaoru, CHANG Xia, WEI Wenjie. Dynamic Frequency Guidance and Semantic Purification Network for Infrared Dim and Small Target Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260582

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Dynamic Frequency Guidance and Semantic Purification Network for Infrared Dim and Small Target Detection

doi: 10.11999/JEIT260582 cstr: 32379.14.JEIT260582

SHEN Xiaoru^{1, 2
,},
CHANG Xia^{1, 2
,
,},
WEI Wenjie^{2, 3}

1.
School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China
2.
Ningxia Key Laboratory of Intelligent Information and Big Data Processing, Yinchuan 750021, China
3.
School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China

Funds: The Natural Science Foundation of Ningxia (2025AAC030002), The Construction Project of First-Class Disciplines in Ningxia Higher Education (NXYLXK2017B09), Graduate Innovation Program of North Minzu University for Nationality(CYX25121)

Received Date: 2026-05-11
Accepted Date: 2026-08-17
Rev Recd Date: 2026-08-14

Available Online: 2026-08-25

Abstract

Abstract

Objective Infrared dim and small target detection remains challenging because targets are small, have low contrast, and are easily obscured by complex background clutter. Although existing methods improve detection performance, several limitations remain, including feature loss during downsampling, insufficient use of physical priors, and contamination of semantic features by background noise. These limitations can result in missed detections and false alarms. To address these problems, a detection network combining dynamic frequency guidance and semantic purification is proposed. Deep semantic features are dynamically coupled with adaptive frequency-domain priors to strengthen target and edge representations, improve detection accuracy, reduce false alarms, and enhance robustness in complex scenes. Methods A framework consisting of a shared encoder and a dual-branch decoder is constructed for infrared dim and small target detection (Fig. 1). First, a Local Target Enhancement (LTA) module is used to preprocess the original infrared image, strengthen small-target boundaries, and suppress background noise. The Sobel operator is then applied to extract edge information and provide a structural prior for subsequent feature learning. During feature extraction, a Learnable Downsampling (LD) module replaces conventional max pooling. Stride-2 convolution adaptively compresses the feature maps while preserving target details and reducing information loss. Residual blocks and progressive multiscale feature fusion are also used to strengthen semantic representations at different levels. A Dynamic Frequency Guidance Module (DFGM) is then introduced to enhance high-frequency details and target-edge information. Deep semantic features are used to dynamically predict the cutoff radius of a high-pass filter. A content-adaptive high-pass filter mask is constructed in the frequency domain to suppress low-frequency components. The filtered features are subsequently transformed back to the spatial domain to generate a frequency guidance map. To suppress spurious background responses introduced during frequency-domain enhancement, a Semantic Purification Module (SPM) is further designed. The deepest semantic features are used to generate a spatial attention map for top-down unidirectional filtering of the frequency guidance features. Background interference is thereby suppressed without contaminating high-level semantic representations with low-level noise. Finally, the dual-branch decoder reconstructs the target region and edge structure, thereby improving segmentation consistency, target boundary localization, and target-shape recovery. Results and Discussions Experimental results on the SIRST-Aug and IRSTD-1k datasets show that the proposed method outperforms mainstream comparison methods. On SIRST-Aug (Table 1), the method achieves the best Iintersection over Union (IoU), normalized Intersection over Union (nIoU), area under the receiver operating characteristic curve (AUC), and probability of detection (P_d), reaching 76.66%, 73.20%, 94.45%, and 99.17%, respectively. The false-alarm rate (F_a) is maintained at 33.96 × 10^–6. On IRSTD-1k (Table 1), the method achieves the best IoU, AUC, and F_a values of 67.17%, 89.52%, and 10.62 × 10^–6, respectively, while obtaining an nIoU of 67.67% and a P_d of 92.59%. Qualitative comparisons further show that the proposed method provides more accurate target localization and more complete target-shape segmentation under complex background interference (Figs. 4 and 5). The model contains 2.94 M parameters, requires 14.68 G FLoating-point OPerations (FLOPs), and has an average inference time of 7.54 ms, providing a favorable balance between detection performance and computational cost (Table 2). Ablation experiments further confirm the contributions of the key modules (Table 3). Adding DFGM increases AUC from 93.17% to 94.60%. Introducing LD increases nIoU to 73.32%, and adding SPM further increases IoU to 76.66%. The loss-function ablation results also show that the combined mask and edge losses provide a better balance among segmentation accuracy, target boundary localization, and false-alarm suppression (Table 4). Overall, the proposed method provides strong detection performance and effective false-alarm suppression. Conclusions An infrared dim and small target detection network based on dynamic frequency guidance and semantic purification is proposed. Through the coordinated use of LD, DFGM, and SPM, target details are effectively preserved, high-frequency structural information is strengthened, and semantic representations are purified. Background interference is consequently suppressed, and target-boundary recovery is improved. Experiments on SIRST-Aug and IRSTD-1k demonstrate that the proposed method outperforms existing mainstream methods across multiple evaluation metrics and maintains strong robustness in complex scenes. Future work can incorporate richer physical priors and self-supervised learning strategies to further improve model generalization and robustness under challenging conditions.
- Infrared dim and small target detection,
- Dynamic frequency guidance,
- Semantic purification,
- Attention mechanism

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References(27)

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