Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1)

Thierry Bouwmans; Anastasia Zakharova; Belmar Garcia; Marwa Chendeb El Rai; Meghna Kapoor; Badri Narayan Subudhi

cs.CVOct 13, 2026

Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1)

Authors: Thierry Bouwmans, Anastasia Zakharova, Belmar Garcia, Marwa Chendeb El Rai, Meghna Kapoor, Badri Narayan Subudhi

Organizations: Laboratoire MIA La Rochelle Université, La Rochelle, 17000, France · ISEN Toulon , Toulon, , France · Laboratoire L3i La Rochelle Université, La Rochelle, 17000, France · Department of Electrical Engineering IIT Jammu, Jagti, 181221, India

Abstract

Infrared small target detection is often used in different civilian and military applications to detect static or moving targets in scenes like in ground, air and sea surveillance. Research in this field started more than three decades ago, and the last two decades have witnessed very significant publications in this field following the rise of low-rank and sparse decomposition (model-based methods) and deep learning models (data-based methods). Furthermore, several surveys can be found in the literature but none of them addresses an overall review in this field. In this context, the aim of this paper is to provide a complete survey of the traditional and recent approaches in infrared small target detection. First, we categorize the different approaches found in the literature in terms of the modeling models, i.e mathematical models, machine learning models and signal processing models. Then, we have discussed them in terms of the critical situations that they claim to handle. Furthermore, we present the available resources, datasets and libraries. Then, we conclude with several promising directions for future research. For the sake of reading, this survey is divided into two parts: 1) "Traditional and Recent Approaches for Infrared Small Target Detection: Overview, Challenges and Perspectives (Part 1)" about conventional and low-rank and sparse decomposition (model-based methods), and 2) "Traditional and Recent Approaches for Infrared Small Target Detection: Overview, Challenges and Perspectives (Part 2)" about deep learning models (data-based methods).

Figures & tables

Surveys [Authors (Date)]	Surveyed Categories (Number of Reviewed Papers)
Bai et al. ( 47 ) (2011)	Traditional Unsupervised Models (30 papers)
Zhang and Peng. ( 48 ) (2019)	Low-rank and Sparse Decomposition Models (70 papers)
Eysa and Hamdulla ( 49 ) (2019)	Typical Algorithm Principles and Characteristics
Zhao et al. ( 51 ) (2022)	Single Frame Models
Cheng et al. ( 52 ) (2024)	Deep Learning Supervised Models (90 papers)
Kumar and Singh ( 53 ) (2025)	All Categories (90 papers) + Datasets

Table 1: Existing Surveys in Infrared Small Target Detection: An Overview.

Figure 1: Traditional Approaches, Low-rank plus Sparse Decomposition (Model-based) and Deep Neural Networks (Data-based models) : An Overview (with representative models).

Panel of Figure 2 — Figure 2: From left to right: Ground environment, Air environment, Sea environment.

Figure 3: Near-Infrared (NIR), Short-Wave Infrared (SWIR), Mid-Wave Infrared (MWIR), and Long-Wave Infrared (LWIR).

Category	Acronyms
Transform Domain Methods	PFT (Qi et al. ( 214 ) ), PQFT (Qi et al. ( 215 ) ), QDCT (Zhang et al. ( 213 ) )
	NSST-PQFT (Ren et al. ( 225 ) )
Classification Methods	Bi et al. ( 157 ) , Wu et al. ( 159 )
	DensityPeaksIR (Huang et al. ( 226 ) ), HSDW (Liu et al. ( 158 ) ), Sample Data (Fan et al. ( 227 ) )
Clustering Methods	Monte Carlo Linear Clustering (Li et al. ( 160 ) )
	Hyperbolic Density Clustering: HDC (Zhu et al. ( 164 ) ), PA-AHDPC (Zhu et al. ( 165 ) ), HWM-Det (Zhu et al. ( 166 ) )

Table 2: Conventional Unsupervised Methods (Part 2): Overview of Single-Frame Detection (28 papers).

Category	Acronyms
Multi-Frame Cumulation Algorithms	Higher-Order Cumulant (Lu ( 217 ) ), Morphology and High-order Statistics (Tang et al. ( 218 ) )
	Self-Adaptive Caliber Temporal-Spatial Filtering (Fan et al. ( 219 ) )
	Ring Perturbation non-uniformity Correction (Fan et al. ( 220 ) )
	Local Energy Center: IABP (Fan et al. ( 221 ) )
Miscellaneous Algorithms	Information Fusion:IF-KL (Peng et al. ( 222 ) )
	Antivibration Pipeline-Filtering: APF (Wang et al. ( 223 ) )

Table 3: Conventional Unsupervised Methods: Overview of Multi-Frame Detection (8 papers).

Category	Methods	Acronyms
Consistency of Backgrounds	Top-Hat Filters	Top-Hat (Jackway ( 183 ) )
		Study (Soni et al. ( 241 ) ), Optimizing the structuring element: Morphological Neural Networks (Zeng et al. ( 171 ) ), Morphological Genetic Algorithm (Zeng et al. ( 171 ) )
		WTH (Bai et al. ( 173 ) ), BTH (Bai et al. ( 173 ) )
		MWTH (Bai et al. ( 242 ) ), MBTH (Bai et al. ( 242 ) ), TEO (Bai et al. ( 174 ) )
		Dynamic Queue (Wang et al. ( 185 ) ), QGA (Deng et al. ( 172 ) )
		Multi-stage Morphological (NTFH) (Yavari ( 186 ) ), Weighted Adaptive Ring (WARTH) (Li et al. ( 68 ) ), Balanced Ring Top-Hat (Zhu et al. ( 176 ) )

Table 4: Conventional Unsupervised Methods (Part 1): Overview of Single-Frame Detection (123 papers).

Figure 4: Illustration of the decomposition of an observed matrix $A$ into a low rank matrix $L$ and a sparse matrix $S$ .

Category	Acronyms
RPCA Matrix Decomposition	IPI [Single Subspace] (Gao et al. ( 336 ) ), NOP [Non-Overlapping Patch](Yang et al. ( 339 ) )
Nuclear-norm Defects	TV-PCP [Single Subspace](Wang et al. ( 344 ) )
	SBL (Guo et al. ( 345 ) )
$l_{1}$ -norm Defects	GRLA (Zhou et al. ( 346 ) ), DGRLA (Zhou et al. ( 347 ) ), $l_{1}$ – $l_{2}$ norm-TV (Shao et al. ( 342 ) )
	LMRPCA (Zhou et al. ( 348 ) ), TV-IIPI (Wan et al. ( 349 ) ), PUOT (Bertrand et al. ( 350 ) )
	HQ-RPCA (Liu et al. ( 351 ) )

Table 5: Single-Frame Detection Unsupervised Matrix Decomposition Methods : An Overview (69 papers).

Methods	Decomposition	Minimization	Constraints	Solver	Convexity	Complexity
IPI (2013)	$A=L+S$	$\underset{L,S}{\text{min}}~~\|\|L\|\|_{*}+\lambda\|\|S\|\|_{l_{1}}$	$A-L-S=0$	Inexact ALM ( 413 )	Yes	$O_{iter}(mnr)$
Gao et al. ( 336 )
LRSR (2015)	$A=AZ+DS+E$	$\underset{Z,S,E}{\text{min}}~~\|\|AZ\|\|_{*}+\lambda\|\|E\|\|_{l_{1}}+\beta\|\|S\|\|_{l_{1}}$	$A-AZ-DS-E=0$	Inexact ALM ( 413 )	Yes	$O_{iter}(r^{3}+nr^{2}+mnr)$
He et al. ( 413 )			$D$ denotes the over-complete dictionary
T-BS (2015)	$A=L+S+E$	$\underset{L,S,E}{\text{min}}~~\|\|L\|\|_{*}+\lambda\|\|S+E\|\|_{l_{1}}$	$A-L-S-E=0$	Landweber Algorithm ( 410 )	Yes	$O_{iter}(r^{3}+nr^{2}+mnr)$
Wang and Qin ( 410 )			$S=S_{+}+S_{-}$ , $E=E_{+}+E_{-}$

Table 6: Low-rank plus Sparse Matrix Decomposition for Infrared Small Target Detection: An homogeneous overview (Part 1).

m\times n

is the size of the observation matrix, and

r

is the rank.

Methods	Decomposition	Minimization	Constraints	Solver	Convexity	Complexity
MoG-MRF (2018)	$A=L+S$	$\underset{L,S}{\text{min}}~~\|\|L\|\|_{*}+\lambda\|\|S\|\|_{l_{1}}$	$A-L-S=0$	Variational Bayesian ( 427 )	No	$O_{iter}((m+n)r^{3}+kmnr)$
Gao et al. ( 414 )
R $S_{1/2}$ NIPI (2019)	$A=L+S+E$	$\underset{L,S}{\text{min}}~~\|\|L\|\|_{S_{1/2}}^{1/2}+\lambda\|\|S\|\|_{l_{1,W_{E}}}$	$A-L-S-E=0$ , $\|\|E\|\|\leq\eta$	ADMM ( 355 )	No	$O_{iter}(mn+mn\min(m,n)+mnr+mr)$
Zhou et al. ( 355 )		$W_{E}=\{w_{E,ij}\}$ : Weights for every entry in the target patch-image matrix
MW-IPI (2019)	$A=L+S$	$\underset{L,S}{\text{min}}~~\|\|L\|\|_{*}+\lambda\|\|S\|\|_{l_{1}}$	$A-L-S=0$	ADMM ( 375 )	No	$O_{iter}(mn+mn\min(m,n)+mnr+mr)$
Li et al. ( 375 )

Table 7: Low-rank plus Sparse Matrix Decomposition for Infrared Small Target Detection: An homogeneous overview (Part 2).

m\times n

is the size of the observation matrix, and

r

is the rank.

Methods	Decomposition	Minimization	Constraints	Solver	Convexity	Complexity
HiLV-LRSD (2023)	$A=L+S$	$\underset{L,S}{\text{min}}~~\|\|L\|\|_{LV*}+\lambda_{1}\|\|S\|\|_{l_{v},l_{1}}$	$A-L-S=0$	PI-ADMM ( 361 )	No	$O_{iter}(mn\min(m,n)+mnr+mn+\min(m,n))$
Liu et al. ( 361 )
LMRPCA (2023)	$A=L+S$	$\underset{L,S}{\text{min}}~~\|\|L\|\|_{*}+\lambda_{1}\Sigma_{i}g_{i}\|\|S_{i}-\tilde{S_{i}}\|\|_{l_{1}}+\lambda_{2}\Sigma_{i}g_{i}\|\|S_{i}\|\|_{l_{1}}$	$(1-S)\odot(A-L)=0$	ADMM ( 348 )	No	$O_{iter}(mn\min(m,n)+mn)$
Zhou et al. ( 348 )
RCTVW (2023)	$A=L+S$	$\underset{L,S}{\text{min}}~~\|\|L\|\|_{RCTV}+\lambda_{1}\|\|S\|\|_{w,l_{1}}$	$A-L-S=0$	ADMM ( 359 )	No	$O_{iter}(mr+mn+nr\min(n,r))$
Liu et al. ( 359 )

Table 8: Low-rank plus Sparse Matrix Decomposition for Infrared Small Target Detection: An homogeneous overview (Part 3).

m\times n

is the size of the observation matrix, and

r

is the rank.

Figure 5: Illustration of the decomposition of an observed tensor $\mathcal{A}$ into a low rank tensor $\mathcal{L}$ and a sparse tensor $\mathcal{S}$ .

Category	Methods	Acronyms
RPCA Tensors Decomposition
	Background	Improved IPI (Deng et al. ( 436 ) ), ASTTV (Liu et al. ( 408 ) ), STFF [Tubal] (Lu et al. ( 437 ) )
	Target	ReIPT (Dai and Wu ( 337 ) ), ReWIPI (Guo et al. ( 438 ) ), WSNM-STIPT (Sun et al. ( 439 ) )
		Facet-LRSD (Dong et al. ( 440 ) ), FDMDEA-STT (Pang et al. ( 441 ) ), STC-LRSD (Fan et al. ( 442 ) )
		STTM-SFR (Pang et al. ( 443 ) ), ReIPT-LPM (Li et al. ( 444 ) )
		WE [Entropy] (Liu and Wang ( 445 ) ), STBMPT (Aliha et al. ( 446 ) ), Twist Tensor (Li et al. ( 447 ) )

Table 9: Multi-Frame Detection Unsupervised Tensor Decomposition Methods: An Overview (90 papers).

Methods	Decomposition	Minimization	Constraints	Solver	Convexity	Complexity
ReIPT (RIPT) (2017)	$\mathcal{A}=\mathcal{L}+\mathcal{S}+\mathcal{E}$	$\underset{\mathcal{L},\mathcal{S}}{\text{min}}~~\Sigma_{i=1}^{3}\|\|\mathcal{L}_{i}\|\|_{*}+\lambda\|\|\mathcal{W}_{LS}\odot\mathcal{S}\|\|_{l_{1}}$	$\|\|\mathcal{A}-\mathcal{L}-\mathcal{S}\|\|_{F}\leq\delta$	ADMM ( 337 )	No	$O_{iter}(mn\min(m,n)+m^{3/2}n+mn)$
Dai and Wu ( 337 )		$\mathcal{W}_{LS}$ (sparsity enhancement weight tensor)
WNRIPT (2018)	$\mathcal{A}=\mathcal{L}+\mathcal{S}$	$\underset{\mathcal{L},\mathcal{S}}{\text{min}}~~\|\|\mathcal{L}\|\|_{\mathcal{W}_{L},*}+\lambda\|\|\mathcal{W}_{S}\odot\mathcal{S}\|\|_{l_{1}}$	$\mathcal{A}-\mathcal{L}-\mathcal{S}=0$	ADMM/IALM ( 455 )	No	$O_{iter}(m^{3/2}n+mn)$
Sun et al. ( 455 )		$\mathcal{W}_{L}$ , $\mathcal{W}_{S}$ (weight tensors)
PSTNN (2019)	$\mathcal{A}=\mathcal{L}+\mathcal{S}$	$\underset{\mathcal{L},\mathcal{S}}{\text{min}}~~\|\|\mathcal{L}\|\|_{PSTNN}+\lambda\|\|\mathcal{W}\odot\mathcal{S}\|\|_{l_{1}}$	$\mathcal{A}-\mathcal{L}-\mathcal{S}=0$	ADMM ( 163 )	No	$O_{iter}(m^{3/2}n+mn)$
Zhang and Peng ( 163 )		$\mathcal{W}$ (weight tensor)

Table 10: Low-rank plus Sparse Tensor Decomposition for Infrared Small Target Detection: An homogeneous overview (Part 1).

\mathcal{A}\in\mathbb{R}^{m\times n\times d}

is the observation tensor, where

m\times n

is the size of each frontal slice and

d

is the number of slices (frames or patches), and

r

is the tensor (tubal) rank.

Methods	Decomposition	Minimization	Constraints	Solver	Convexity	Complexity
STFFT (2025)	$\mathcal{A}=\mathcal{L}+\mathcal{S}$	$\underset{\mathcal{L},\mathcal{S}}{\text{min}}~~\|\|\mathcal{L}\|\|_{PTNN}+\lambda_{1}\|\|\mathcal{W}\odot\mathcal{S}\|\|_{l_{1}}$	$\mathcal{A}-\mathcal{L}-\mathcal{S}=0$	ADMM ( 437 )	No	$O_{iter}(mnd\min(m,n)+mnd)$
Lu et al. ( 437 )		$\mathcal{W}$ (sparsity weight tensor)
ASTIT-WPTAR (2025)	$\mathcal{A}=\mathcal{L}+\mathcal{S}+\mathcal{E}$	$\underset{\mathcal{L},\mathcal{S}}{\text{min}}~~\Sigma_{k=1}^{3}\|\|\mathcal{L}^{T_{k}}\|\|_{wp,wa}+\lambda\|\|\mathcal{S}\|\|_{l_{p}}^{p}$	$\|\|\mathcal{A}-\mathcal{L}-\mathcal{S}\|\|_{F}\leq\delta$	ADMM ( 476 )	No	$O_{iter}(mnd\min(m,n)+mnd^{2}+mnd)$
Sun et al. ( 476 )		$\mathcal{L}^{T_{k}}$ : $k$ -th mode transform of $\mathcal{L}$ .
5D-STFC (2025)	$\mathcal{A}^{5D}=\mathcal{L}^{5D}+\mathcal{S}^{5D}+\mathcal{E}^{5D}$	$\underset{\mathcal{L}^{5D},\mathcal{S}^{5D},\mathcal{E}^{5D}}{\text{min}}~~\|\|\mathcal{L}^{5D}\|\|_{STF\text{-}N}+\lambda_{1}\|\|\mathcal{W}^{5D}\odot\mathcal{S}^{5D}\|\|_{STK\text{-}S}+\lambda_{2}\|\|\mathcal{E}^{5D}\|\|_{F}^{2}$	$\mathcal{A}^{5D}-\mathcal{L}^{5D}-\mathcal{S}^{5D}-\mathcal{E}^{5D}=0$	Multi-Block ADMM ( 477 )	No	$O_{iter}(mnd\min(m,n)+mnd)$
Luo et al. ( 477 )		$\mathcal{A}^{5D}$ : 5-D tensor; $d$ : product of non-spatial modes.

Table 11: Low-rank plus Sparse Tensor Decomposition for Infrared Small Target Detection: An homogeneous overview (Part 2).

\mathcal{A}\in\mathbb{R}^{m\times n\times d}

is the observation tensor, where

m\times n

is the size of each frontal slice and

d

is the number of slices (frames or patches), and

r

is the tensor (tubal) rank.

Datasets (Authors) (Date)	Background Features	Target Features	Images/Videos/Resolution	Single/Multi-Frame	Sparse/ Dense
Visual Modality Datasets (28)
NUST-SIRST Dataset (Wang et al. ( 526 ) ) (2019)	Sky, clouds, ground, buildings	Tiny cats, cars, drones, and planes	100 images, 11 sequences (2,098 frames), $256\times 256$	Single/Multi-Frame	Sparse
DAUB Dataset (Hui et al. ( 528 ) ) (2019)	Sky and complex environments	Dim fixed-wing UAVs	22 sequences, 16,177 frames	Multi-Frame	Sparse
NUAA-SIRST Dataset (Dai et al. ( 529 ) ) (2021)	Sea, clouds and city environments	Extremely dim, tiny targets	427 images, variable resolutions	Single-Frame	Sparse
NUDT-SIRST Dataset (Li et al. ( 151 ) ) (2021)	Cloud, city, sea and field environments	Ships, planes, UAVs	1,327 synthesized images, $256\times 256$	Single-Frame	Sparse
ISDD Dataset (Han et al. ( 544 ) ) (2021)	Complex ocean, inshore and offshore environments	Ships	1,284 images, $500\times 500$	Single-Frame	Mixed

Table 12: Existing Datasets in Infrared Small Target Detection: An Overview (32 Datasets).

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Infrared small target detection finds faint, often sub-pixel objects against cluttered thermal backgrounds while limiting false alarms.

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Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 2)
Oct 13, 2026Thierry Bouwmans, Anastasia Zakharova, Belmar Garcia +3Infrared Small Target Detection
Laboratoire MIA La Rochelle Université, La Rochelle, 17000, France · ISEN Toulon , Toulon, , France · Laboratoire L3i La Rochelle Université, La Rochelle, 17000, France +1
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Department of Automation, Tsinghua University, Beijing, 100190, China. · School of Artificial Intelligence, Beijing Institute of Technology, Beijing, 100081, China. · School of Information and Communication Engineering and the Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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Oct 21, 2026Xuelin Qian, Zixuan Wang, Chang Xu +2Infrared Small Target Detection Diffusion Models

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Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 2)

Thierry Bouwmans, Anastasia Zakharova, Belmar Garcia, Marwa Chendeb El Rai+2

Laboratoire MIA La Rochelle Université, La Rochelle, 17000, France · ISEN Toulon , Toulon, , France · Laboratoire L3i La Rochelle Université, La Rochelle, 17000, France+1

Infrared Small Target Detection

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Since the rise of supervised deep learning models, researchers have attempted to solve infrared small target detection using various models like convolutional neural networks, GANs, Transformers, Mixture of Experts and recent graph/hypergraph neural networks. Moreover, these deep neural network methods have surpassed the traditional unsupervised models and the low-rank and sparse decomposition unsupervised models reviewed in "Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1)". In this context, the aim of this paper is to provide a complete survey of the deep learning models in infrared small target detection. First, we categorize the different approaches found in the literature in terms of their modeling strategies. Then, we discuss them in terms of the critical situations that they claim to handle. Furthermore, we present the specific performance evaluation protocols. Then, we conclude with several promising directions for future research.

Abstract

Infrared Small Target Detection

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