Frequency-domain Decoupling and Spatial-Prior-constrained Detection Method for Infrared Dim and Small Targets

LIU Minglong; JIANG Tingyao; LI Yulan

doi:10.11999/JEIT260847

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LIU Minglong, JIANG Tingyao, LI Yulan. Frequency-domain Decoupling and Spatial-Prior-constrained Detection Method for Infrared Dim and Small Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260847

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LIU Minglong, JIANG Tingyao, LI Yulan. Frequency-domain Decoupling and Spatial-Prior-constrained Detection Method for Infrared Dim and Small Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260847

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Frequency-domain Decoupling and Spatial-Prior-constrained Detection Method for Infrared Dim and Small Targets

doi: 10.11999/JEIT260847 cstr: 32379.14.JEIT260847

College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

Funds: The National Key R&D Program of China (2024YFD1702005), The National Natural Science Foundation of China (U25A20403), The Special Fund for Central Government Guiding Local Science and Technology Development of Hubei Province (2024BSB002)

Received Date: 2026-06-23
Accepted Date: 2026-08-17
Rev Recd Date: 2026-08-14

Available Online: 2026-08-26

Abstract

Abstract

Objective Infrared dim and small target detection exploits thermal-radiation differences between targets and backgrounds for passive sensing in air-ground inspection, maritime surveillance, and wide-area warning. Under long-range imaging, low signal-to-noise ratios, and complex backgrounds, targets occupy few pixels and exhibit weak textures, blurred contours, and low contrast. Bounding-box detection directly predicts class confidence and target locations, making it suitable for rapid edge deployment and subsequent tracking initialization. The Real-Time DEtection TRansformer (RT-DETR) provides an efficient end-to-end framework. However, its application to infrared dim and small targets has three limitations. Repeated downsampling weakens local details and initial localization cues. Intra-scale global interaction does not sufficiently distinguish high-frequency target responses from low-frequency background context. In addition, cross-scale fusion may propagate homogeneous clutter because of insufficient explicit spatial constraints. Therefore, a Frequency-domain decoupling and Spatial-Prior-constrained DETR (FSP-DETR) is proposed to coordinate shallow detail preservation, target-background decoupling, and localization constraints. Methods FSP-DETR is built on RT-DETR-R18 and follows a pipeline of backbone feature extraction, intra-scale interaction, cross-scale fusion, and query-based decoding (Fig. 1). Three modules are introduced at successive stages to address the identified limitations. First, a Subspace Progressive Attention Modulated Inverted Residual Block (SPA-MIRB) is embedded in the Residual Network 18 (ResNet18) backbone to preserve shallow details (Fig. 2). Its inverted-residual branch combines pointwise and depthwise convolutions with a residual connection to retain target edges, spots, and weak textures. Its attention branch partitions the channels into multiple subspaces and progressively transfers interaction results across them to strengthen weak target responses. Second, a Frequency-Domain asymmetrically decoupled Attention-based Intra-scale Feature Interaction module (FD-AIFI) replaces the Attention-based Intra-scale Feature Interaction module (AIFI) (Fig. 3). The original unified global interaction is divided into a high-pass local attention path and a low-pass global attention path. The high-pass path applies attention within local windows to preserve compact target responses and limit interference from distant clutter. The low-pass path retains the original spatial resolution of the query features but pools the key and value features. This design compresses low-frequency context while preserving spatial queries and target-position indices. The outputs of the two paths are concatenated along the channel dimension and then linearly projected. Third, a Spatial-Prior-Constrained CNN-based Crossscale Feature-fusion Module (SPC-CCFM) retains the original upsampling, concatenation, and convolution path and introduces a High-order Spatial Representation Module (HSRM) and an Overall-level Mapping Injection Mechanism (OMIM). HSRM aligns multilevel features and constructs spatial passthrough, high-order spatial relation, and low-order fidelity branches (Fig. 4). These branches preserve the original spatial responses, model spatially separated but response-similar clutter through hypergraph aggregation, and supplement local edge and weak-texture information, respectively. OMIM performs scale mapping and channel adaptation according to each fusion node and injects the resulting spatial prior into the corresponding node to constrain cross-scale feature refinement. Results and Discussions Experiments are performed on IRSTD-1k and NUAA-SIRST, with pixel-level masks converted into single-class minimum bounding rectangles. Component ablation shows that SPA-MIRB, FD-AIFI, and SPC-CCFM each improve Mean average precision at an Intersection over Union threshold of 0.5 (mAP@0.5) on both datasets (Table 1). FSP-DETR achieves mAP@0.5 values of 89.03% and 98.75% on IRSTD-1k and NUAA-SIRST, respectively, exceeding RT-DETR-R18 by 3.39 and 3.08 percentage points. The parameter count decreases from 19.87 million to 17.24 million, and the computational cost decreases from 56.9 to 50.4 billion floating-point operations, corresponding to reductions of 13.2% and 11.4%, respectively. Although some variants achieve higher precision or recall at a single operating point, the complete model obtains the highest mAP@0.5 with lower complexity. Replacement experiments further support the structural choices. SPA-MIRB and FD-AIFI achieve the highest mAP@0.5 among their respective alternatives. SPC-CCFM exceeds the two alternative fusion modules by 0.48 and 0.26 percentage points, respectively, indicating that its main benefit is stronger spatial constraint rather than complexity reduction (Table 2). SPA-MIRB reaches accuracy-complexity trade-off. A computation-allocation factor a = 0.50 yields the best performance, with mAP@0.5 and mAP@0.5:0.95 reaching 97.27% and 53.04%, respectively, on NUAA-SIRST (Table 3). Attention maps show that the high-pass path concentrates its responses around target neighborhoods, whereas the low-pass path produces broader and smoother responses that cover more surrounding background regions (Fig. 5). Under unified experimental settings, FSP-DETR achieves the highest mAP@0.5 on both datasets, with a frame rate of 172 frame/s and an average per-image forward time of 5.81 ms (Table 4). It also outperforms deeper RT-DETR variants with fewer parameters, lower computational cost, and shorter forward time. These results demonstrate the advantage of task-oriented structural adaptation over simply enlarging the backbone. Detection visualizations show better agreement between predicted bounding boxes and target regions in cluttered scenes (Fig. 6). Instance-level error analysis shows that the missed-detection rate decreases from 15.70% to 13.22% on IRSTD-1k and from 10.71% to 5.36% on NUAA-SIRST. The average number of false positives per image decreases from 0.490 to 0.380 and from 0.209 to 0.070, respectively (Table 5). Normalized center-offset errors also decrease on both datasets. However, the scale error on NUAA-SIRST increases slightly from 10.63% to 11.05%, indicating that scale regression for extremely small targets still requires improvement. Conclusions FSP-DETR coordinates shallow detail preservation, frequency-domain decoupled intra-scale interaction, and spatial-prior-constrained cross-scale fusion for end-to-end bounding-box detection. The model improves detection accuracy, reduces missed detections and false positives, and decreases normalized center-offset error. At the same time, model complexity is reduced and efficient inference is maintained. A favorable balance between detection accuracy and efficiency is therefore achieved in complex infrared scenes. Future work will investigate multi-frame spatiotemporal modeling for thermal crossover backgrounds and dense dim and small target scenes.
- Infrared dim and small target detection,
- Real-Time Detection Transformer(RTDT),
- End-to-end detection,
- Frequency-domain decoupling,
- Spatial prior constraint

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