Rethinking IRSTD: Single-Point Supervision Guided Encoder-only Framework is Enough for Infrared Small Target Detection

Rixiang Ni; Boyang Li; Jun Chen; Yonghao Li; Feiyu Ren; Yuji Wang; Haoyang Yuan; Wujiao He

cs.CVApr 7, 2026

Rethinking IRSTD: Single-Point Supervision Guided Encoder-only Framework is Enough for Infrared Small Target Detection

Authors: Rixiang Ni, Boyang Li, Jun Chen, Yonghao Li, Feiyu Ren, Yuji Wang, Haoyang Yuan, Wujiao He,

Organizations: National University of Defense Technology, Changsha 410003, China

Abstract

Infrared small target detection (IRSTD) aims to separate small targets from clutter backgrounds. Extensive research is dedicated to the pixel-level supervision-guided "encoder-decoder" segmentation paradigm. Although having achieved promising performance, they neglect the fact that small targets only occupy a few pixels and are usually accompanied with blurred boundary caused by clutter backgrounds. Based on this observation, we argue that the first principle of IRSTD should be target localization instead of separating all target region accompanied with indistinguishable background noise. In this paper, we reformulate IRSTD as a centroid regression task and propose a novel Single-Point Supervision guided Infrared Probabilistic Response Encoding method (namely, SPIRE), which is indeed challenging due to the mismatch between reduced supervision network and equivalent output. Specifically, we first design a Point-Response Prior Supervision (PRPS), which transforms single-point annotations into probabilistic response map consistent with infrared point-target response characteristics, with a High-Resolution Probabilistic Encoder (HRPE) that enables encoder-only, end-to-end regression without decoder reconstruction. By preserving high-resolution features and increasing effective supervision density, SPIRE alleviates optimization instability under sparse target distributions. Finally, extensive experiments on various IRSTD benchmarks, including SIRST-UAVB and SIRST4 demonstrate that SPIRE achieves competitive target-level detection performance with consistently low false alarm rate (Fa) and significantly reduced computational cost. Code is publicly available at: https://github.com/NIRIXIANG/SPIRE-IRSTD.

Figures & tables

Figure 1: Motivation of SPIRE for IRSTD. Existing IRSTD methods suffer from three key limitations: (a) heavy pixel-level annotation cost with extremely sparse positive samples, (b) redundant encoder–decoder architectures relying on extensive feature fusion and attention mechanisms, and (c) localization uncertainty caused by segmentation-based predictions. SPIRE reformulates IRSTD as centroid localization with single-point supervision and probabilistic response map modeling, enabling efficient annotation, a lightweight encoder-only framework, and precise peak-based localization.

Figure 2: Performance of SPIRE on SIRST-UAVB. (a) Performance–efficiency trade-off among representative IRSTD methods, where the axes denote F1-score and Precision, and bubble size indicates the number of parameters. (b) Six-dimensional evaluation across Precision, Recall (Pd), F1-score, False Alarm rate, FLOPs, and parameters. SPIRE achieves a favorable balance between detection accuracy and computational efficiency.

Figure 3: Framework of SPIRE (Single-Point Supervision guided Infrared Probabilistic Response Encoding). Given an infrared image with single-point centroid annotations, SPIRE constructs structured supervision through Point-Response Prior Supervision (PRPS), which maps centroids to the feature space and generates Gaussian-like response priors inspired by infrared PSF diffusion. A lightweight High-Resolution Probabilistic Encoder (HRPE) then regresses the probabilistic response map (heatmap). During inference, target locations are obtained by lightweight peak extraction on the predicted heatmap via local-maximum detection, thresholding, and sub-pixel centroid refinement.

Figure 4: Qualitative comparison of SPIRE on SIRST-UAVB and SIRST4. Red rectangles denote ground-truth targets. Red circles indicate true positives, green circles denote false positives, and orange circles correspond to false negatives under the unified centroid protocol.

Method	Venue	SIRST-UAVB (2400:600)				SIRST4 (2285:1067)				FLOPs $\downarrow$	Params $\downarrow$
Method	Venue	Pre $\uparrow$	Rec $\uparrow$	F1 $\uparrow$	Fa $\downarrow$	Pre $\uparrow$	Rec $\uparrow$	F1 $\uparrow$	Fa $\downarrow$
ACM [ 11 ]	$\mathrm{WACV}^{21}$	87.01	71.16	78.29	34.04	90.17	70.38	79.05	44.08	2.51	0.40
ALCNet [ 12 ]	$\mathrm{TGRS}^{21}$	95.06	81.11	87.53	12.72	91.45	72.41	80.82	38.90	2.36	0.43
ISTDU-Net [ 16 ]	$\mathrm{GRSL}^{22}$	79.28	78.08	78.67	61.54	93.28	72.03	81.29	29.83	49.65	2.75
RDIAN [ 24 ]	$\mathrm{TGRS}^{23}$	52.68	77.91	62.86	211.08	90.64	80.83	85.45	47.98	23.24	0.22
DNANet [ 17 ]	$\mathrm{TIP}^{23}$	94.48	89.54	91.95	15.77	93.99	81.20	87.13	29.82	89.13	4.70

Table 1: Comparison with representative methods on SIRST-UAVB and SIRST4. We report Pre(%), Rec (Pd)(%), F1(%), Fa (

10^{-8}

), Params (M), and FLOPs (G). Params and FLOPs are computed at

640\times 640

input resolution.

Method	$\delta=3$			$\delta=5,8,10$			FPS $\uparrow$
Method	Rec $\uparrow$	F1 $\uparrow$	Fa $\downarrow$	Rec $\uparrow$	F1 $\uparrow$	Fa $\downarrow$	FPS $\uparrow$
DNANet [ 17 ]	80.52	86.41	33.72	81.20	87.13	29.82	80.86
SCTransNet [ 37 ]	80.15	81.18	99.42	86.39	83.72	114.98	67.09
SDSNet [ 38 ]	86.39	87.21	67.43	88.27	87.81	73.48	46.97
Ours	92.93	94.74	18.61	94.21	94.60	28.53	261.2

Table 2: Multi-threshold robustness and inference speed comparison on SIRST4. We report Rec (Pd)(%), F1(%), Fa (

10^{-8}

) under centroid-matching thresholds, together with FPS measured under the same input and hardware setting.

Variant	Pre $\uparrow$	Rec $\uparrow$	$F_{1}$ $\uparrow$	Fa $\downarrow$
Supervision form
PRPS	99.82	94.44	97.05	1.02
Single-point impulse	98.71	90.22	94.27	2.85
Unconstrained Gaussian	98.93	93.76	96.28	2.44
PRPS scale sensitivity
PRPS ( $\sigma{=}2,\ r{=}6$ )	99.82	94.44	97.05	1.02

Table 3: Ablation study of PRPS on SIRST-UAVB. The first block compares different supervision forms, while the second block analyzes the scale sensitivity of PRPS. We report Pre(%), Rec (Pd)(%), F1(%), and Fa (

10^{-8}

)

Variant	Pre $\uparrow$	Rec $\uparrow$	$F_{1}$ $\uparrow$	Fa $\downarrow$	FLOPs $\downarrow$	Params $\downarrow$
Resolution preservation (output stride)
HRPE ( $s{=}4$ )	99.82	94.44	97.05	1.02	7.68	29.47
HRPE ( $s{=}2$ )	99.63	91.23	95.25	0.81	26.77	26.56
HRPE ( $s{=}8$ )	90.05	83.98	86.91	22.38	3.05	33.27
Component ablation
HRPE	99.82	94.44	97.05	1.02	7.68	29.47

Table 4: Ablation study of HRPE on SIRST-UAVB. The first block evaluates resolution preservation with different output strides

s

, while the second block analyzes the contribution of HRPE components. We report Pre(%), Rec (Pd)(%), F1(%), Fa (

10^{-8}

), Params (

10^{-2}

M), and FLOPs (G). Params and FLOPs are computed at

640\times 640

input resolution.

Figure 5: Ablation study of PRPS on SIRST-UAVB. Red rectangles denote ground-truth targets. Red circles indicate true positives and orange circles correspond to false negatives under the unified centroid protocol.

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Infrared small target detection (IRSTD) methods predominantly formulate the task as pixel-level segmentation, which requires costly dense annotations and is not well suited to tiny targets with weak texture and ambiguous boundaries. To address this issue, we propose Point-to-Mask, a framework that bridges low-cost point supervision and mask-level detection through two components: a Physics-driven Adaptive Mask Generation (PAMG) module that converts point annotations into compact target masks and geometric cues, and a lightweight Radius-aware Point Regression Network (RPR-Net) that reformulates IRSTD as target center localization and effective radius regression using spatiotemporal motion cues. The two modules form a closed loop: PAMG generates pseudo masks and geometric supervision during training, while the geometric predictions of RPR-Net are fed back to PAMG for pixel-level mask recovery during inference. To facilitate systematic evaluation, we further construct SIRSTD-Pixel, a sequential dataset with refined pixel-level annotations. Experiments show that the proposed framework achieves strong pseudo-label quality, high detection accuracy, and efficient inference, approaching full-supervision performance under point-supervised settings with substantially lower annotation cost. Code and datasets will be available at: https://github.com/GaoScience/point-to-mask.

Abstract

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