Dim Small Target Research Articles

A Spatial-Temporal Feature-Based Detection Framework for Infrared Dim Small Target

The detection of infrared small targets under low signal-to-clutter ratio (SCR) and complex background conditions has been a challenging and popular research topic. In this article, a spatial-temporal feature-based detection framework is proposed. First, several factors, such as the infrared target’s small sample, the sensitive size, and the usual sample selection strategy, that affect the detection of small targets are analyzed. In addition, the small intersection over union (IOU) strategy, which helps to solve the false convergence and sample misjudgment problem, is proposed. Second, aiming at the difficulties due to the target’s dim information and complex background, the interframe energy accumulation (IFEA) enhancement mechanism-based end-to-end spatial-temporal feature extraction and target detection framework is proposed. This framework helps to enhance the target’s energy, suppress the strong spatially nonstationary clutter, and detect dim small targets. Experimental results show that using the small IOU strategy and IFEA mechanism, the proposed multiple frame-based detection framework performs better than some popular deep learning (DL)-based detection algorithms.

DFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution Model

A new deep learning method based on the target gray-level distribution constraint mechanism model is proposed to solve the infrared dim small target detection problem in the complex environment. First, to solve the uneven distribution of positive and negative samples, the designed smoothness operator is used to suppress the background and enhancement target by measuring the difference in their features in 1D and 2D gradient. Second, an infrared dim small target detection network based on dense feature fusion, namely the DFFIR-net network, is proposed. The DFFIR-net enhances the feature expression of dim small targets by integrating the original features and the smoothness features of gray-level gradient. Also, the DFFIR-net alleviates the problem of sparse feature extraction. Finally, a multiscale 2D Gaussian label generation strategy is proposed. This strategy is critical in supervising the training of DFFIR-net in multi-dimensional Gaussian space, improving the feature exploration ability of the network and detection performance under small training samples. The experimental results show that compared with the existing advanced detection methods, the proposed method has higher accuracy and lower false alarm rates in various complex scenes.

Cross-Connected Bidirectional Pyramid Network for Infrared Small-Dim Target Detection

Infrared small-dim target detection is an important technology in the fields of infrared guidance, anti-missile, and tracking system. Due to the small size of targets, no obvious structure information, and low image signal-to-noise ratio (SNR), infrared small-dim target detection is still a challenging task. In this letter, a cross-connected bidirectional pyramid network (CBP-Net) is proposed for infrared small-dim target detection. The main body of the CBP-Net is to embed a bottom-up pyramid in the feature pyramid network (FPN), which is designed to provide more comprehensive target information by connecting with the original multi-scale features and the top-down pyramid. The bottom-up pyramid together with the top-down pyramid forms the proposed bidirectional pyramid structure. Then, an region of interest (ROI) feature augment module (RFA) composed of deformable ROI pooling and position attention is designed to fuse multi-scale ROI features and enhance the spatial information of the small-dim target. Besides, a regular constraint loss (RCL) is introduced to restrict multi-scale feature fusion to learn more precise target location information. Experimental results on two challenging datasets show that the performance of the proposed CBP-Net is superior to the state-of-the-art methods.

Infrared dim small target detection based on visual saliency and local entropy

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed

Visual salient dim small target detection based on nonlinear anti noise estimation

Visual salient dim small target detection based on nonlinear anti noise estimation

Dim and Small Target Detection Based on Spiral Gradient Optimization Estimation and High-Order Correlation Enhancement

In order to reduce the influence of strong halo effect on dim small target detection in daytime, a dim small target detection algorithm based on spiral gradient optimization estimation and high-order correlation enhancement is proposed in this paper. In this paper, we first design a spiral motion model to obtain the local gradient information in the central image point by perturbing the designed motion direction, then estimate the optimal background by establishing a gradient optimization model to achieve background suppression while effectively removing the halo phenomenon. Considering the original high-order correlation model only uses a single pixel for motion correlation, there is insufficient information utilization, an improved high-order correlation energy enhancement model is proposed to enhance the target signal, the algorithm first constructs an attention discrimination model based on inner and outer windows to obtain the salient region of the image, and then carries out multi frame high-order motion correlation of neighborhood blocks to enhance the target energy. After experiments, it is shown that compared with the traditional algorithm, the algorithm proposed in this paper can effectively weaken the halo effect while suppressing most of the background and can effectively enhance the local signal-to-noise ratio of the target.

Spatial–Temporal Stochastic Resonance Model for Dim-Small Target Detection

Stochastic resonance (SR) is usually utilized to enhance the signal with the help of noise. Inspired by this, we find the SR can also handle the problem of dim-small target detection under the low local signal-to-noise ratio (LSNR) situation. In this letter, we propose a novel spatial-temporal stochastic resonance (STSR) model for dim-small target detection. First, we select the SR as the core model to enhance the salience of the target by the inherent strong noise. With the help of the Poisson distribution prior, we employ the multiple adjacent frames as the input of the SR model, improving the LSNR of the resonance state through the temporal accumulation of photons. Then, we introduce the total variation regularization in the variational framework to remove the false alarm points by spatial smoothing, while preserving the role of noise in the SR. Finally, we customize an optimization process based on the alternating direction method of multiplier (ADMM) to solve the STSR variational minimization problem. Both the qualitative and quantitative experiments on real visible and infrared image sequences have demonstrated the superiority of the proposed model, especially in the low LSNR situation below 2 dB.

Adaptive kernel regression and energy concentration criterion for infrared dim small target detection

It is always a challenging task to detect a small target with low signal-to-noise ratio under complex background in infrared images. To address this problem, an effective algorithm based on background subtraction is proposed. First, we add the gradient feature into the kernel regression model to acquire an edge-preserving background estimation. The smoothing matrix of the kernel function is reestablished by a rotation angle and an elongation scale. Further, a multiscale first-order directional derivative filter is presented to calculate these factors adaptively. Second, to segment the real target from the subtracted image, we model the imaging process of the small target using the point spread function of the optical system. According to the analysis of the imaging size and the energy distribution of target, an energy concentration criterion is constructed and used for target extraction. Finally, comparison of experimental results demonstrates that the proposed algorithm achieves robust performances on background suppression and extracts the target accurately with a high detection probability and low false alarm rate.

Infrared small target energy distribution model for subpixel motion.

Dim small target detection is an important application of infrared (IR) searching and tracking systems. IR small target detection methods based on target energy information can accomplish a high detection rate and low false alarm rate. However, the general inaccuracy of target energy distribution models makes most detection algorithms ineffective. In this paper, considering the effect of a target's subpixel motion on energy distribution, a new, to the best of our knowledge, IR small target energy distribution (NSTED) model for subpixel motion is proposed. The NSTED model can well describe energy distribution variation of a subpixel moving target. The simulation results demonstrate that NSTED has a better output signal-to-noise ratio (SNR) than state-of-the-art Gaussian and hyperbolic secant models. Compared with the two models, NSTED improves the output SNR by at least 17.9% and 20.7%, respectively.

Infrared maritime dim small target detection based on spatiotemporal cues and directional morphological filtering

The existing small target detection algorithms will suffer severe performance deterioration when the weak target is submerged in heavy cluttered infrared (IR) maritime image. To settle this problem, a small target detection scheme is developed based on spatiotemporal cues and multidirectional morphological filtering. Initially, considering the small target will be isotropic because of the point spread function of the long-distance thermal imaging, while background edge clutters are generally local directional, the multidirectional structuring elements (MSEs) are constructed. By incorporating constructed MSEs and morphological operations, the directional morphological filtering (DMF) is established to measure the multidirectional differences between target region and local surroundings. Then, according to the brightness and isotropic Gaussian-like shape of small target in local spatial domain, the local intensity difference measure (LIDM) and the local Gaussian-like shape measure (LGSM) are presented to differentiate the small target from background clutters. After that, in order to integrate the two spatial properties, the multidirectional improved top-hat filter (MITHF) is proposed by fusing LIDM and LGSM to highlight potential small targets and suppress strong structural edge clutters. Finally, considering the consistency of targets and the fluctuation of sea clutters in temporal domain, a new appearance descriptor namely histogram of oriented morphological filtering (HOMF) is designed to characterize the small target by fully exploiting the advantages of DMF, and the HOMF-based multi-frame confirmation strategy (HMCS) is developed to further identify real small targets and eliminate false alarms. Extensive experiments prove that proposed algorithm is superior to the compared methods, especially for dim small target buried in intricate maritime background clutter.

Research on region selection super resolution restoration algorithm based on infrared micro-scanning optical imaging model

With spring up of infrared imaging related industry, infrared imaging technology has become mainstream development direction of intelligent photoelectrical detection due to its good concealment, wide detection range, high positioning accuracy, long distant penetration, light weight, little volume, low power dissipation and high solidity. However, the features of infrared dim-small target image such as less details and low SNR become bottleneck of infrared image application. How to enhance imaging effect of infrared dim-small target becomes research hotspot. Starting from the point of ‘restoration as foundation’, the theory and technology of infrared dim-small target super-resolution restoration by utilizing the theory and technology of super-resolution restoration are explored in this paper. This paper mainly focuses on the research of super-resolution restoration algorithm of infrared dim-small target based on infrared micro-scanning optical model. Aiming at solving super-resolution restoration problem of infrared dim-small target, the traditional super-resolution restoration algorithm is optimized and the improved algorithm is proposed. Meanwhile, infrared micro-scanning optical model is introduced to break theoretical limit of simple image processing algorithm. And the performance of infrared image super-resolution restoration is improved.

Background Modeling Based on Statistical Clustering Partitioning

In order to effectively detect dim-small targets in complex scenes, background suppression is applied to highlight the targets. This paper presents a statistical clustering partitioning low-rank background modeling algorithm (SCPLBMA), which clusters the image into several patches based on image statistics. The image matrix of each patch is decomposed into low-rank matrix and sparse matrix in the SCPLBMA. The background of the original video frames is reconstructed from the low-rank matrices, and the targets can be obtained by subtracting the background. Experiments on different scenes show that the SCPLBMA can effectively suppress the background and textures and equalize the residual noise with gray levels significantly lower than that of the targets. Thus, the difference images obtain good stationary characteristics, and the contrast between the targets and the residual backgrounds is significantly improved. Compared with six other algorithms, the SCPLBMA significantly improved the target detection rates of single-frame threshold segmentation.

Infrared Small Maritime Target Detection Based on Integrated Target Saliency Measure

Robust and effective detection of a small target in an infrared maritime image is a key technology of maritime target search and tracking applications. Infrared small target detection is a challenging task due to the factors such as dim small targets and various complex backgrounds caused by sun glitters and strong waves. In this article, the integrated target saliency measure (ITSM) based on local and nonlocal spatial information is proposed to improve target detection performance. We combine local heterogeneity property of targets and nonlocal self-correlation property of background with targets' sparsity to separate real targets from background clutters. First, local heterogeneity calculation based on cross-window standard deviation (CSD) is proposed to extract candidate targets preliminarily, which enhances the local intensity difference between small targets and neighboring background. Meanwhile, low-rank representation (LRR) is applied to background prediction and removal, which is followed by adaptive threshold segmentation to enhance target saliency. Finally, we integrate the results obtained from the two steps mentioned above to further enhance targets and suppress background clutters. Then, real targets are extracted by an iterative threshold on the integrated map so as to generate the seed map, in addition, the target expansion strategy is exploited to keep full target areas. Experimental results on six datasets show that the proposed method outperforms the state-of-the-art methods in terms of robustness and target detection accuracy.

Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1,1}$</tex-math></inline-formula> -norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1,1}$</tex-math></inline-formula> matrix norm and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis.

Background suppression for infrared dim small target scene based on adaptive gradient reciprocal filtering

Background suppression for infrared dim small target scene based on adaptive gradient reciprocal filtering

Infrared small dim target detection using local contrast measure weighted by reversed local diversity

Infrared small dim target detection using local contrast measure weighted by reversed local diversity

Multidirectional Ring Top-Hat Transformation for Infrared Small Target Detection

Fast and robust detection of a small target in an infrared image is one of the key techniques in infrared search and track systems. The top-hat transformation is an important technology in the field of infrared target detection. Many algorithms have been proposed to improve the top-hat transformation by modifying different structural elements. However, these methods can cause a high false alarm rate under conditions of small dim targets, low signal-to-noise, and complex background. To overcome this limitation, considering both detection performance and speed, an effective and robust detection model using multidirectional structural elements is proposed in this article. First, the nonconcentric multidirectional ring structural elements are designed. Then, using the morphological transformation with designed structural elements, the background is suppressed, and a small target is enhanced in different directions. Finally, the results of the morphological transformations are fused to obtain the final result, further highlighting a small target. The experimental results demonstrate the robust performance of the proposed method for various backgrounds and targets. In addition, the proposed method can achieve a detection speed of up to 10.4 ms per frame under the image resolution of 240 $ \times $ 320 pixels. Therefore, the proposed method is suitable for real-time applications.

Dim-small targets detection of infrared images in complex backgrounds

红外弱小目标检测是安防监控、侦察探测、精确制导等领域的关键技术。为了提高复杂背景条件下红外弱小目标检测的准确性和实时性，提出了一种基于深度学习的红外弱小目标检测算法YOLO-FCSP。根据红外图像中弱小目标的特点，在YOLO检测框架的基础上，通过减少下采样次数，结合跨阶段局部模块、Focus结构和空间金字塔池化结构设计了特征提取网络。借鉴多路径聚合的思路优化特征融合网络，同时调整检测输出层数量，通过信息复用提高特征利用效率。实验结果表明，本文提出的算法在检测红外弱小目标时具有较高的准确率和检测速度，精度和召回率分别为91.9%和94.6%，平均准确率(AP)值达到92.6%，检测速度达到170 f/s，满足实际应用中实时检测的需求。

CNN‐based infrared dim small target detection algorithm using target‐oriented shallow‐deep features and effective small anchor

CNN‐based infrared dim small target detection algorithm using target‐oriented shallow‐deep features and effective small anchor

A method for single frame detection of infrared dim small target in complex background

In order to solve the problem of high false alarm rate caused by strong disturbance (the ground objects, uncertain strong noise) in complex background, we propose a novel algorithm based on density peaks searching and improved region growing. Initially, density peaks searching is employed to select the candidate targets quickly; then, improved region growing with introducing the relative local contrast and the local gray average is used to extract the features of candidate targets; finally, an adaptive threshold is applied to extract real targets. Experimental results on two real sequences show that the proposed algorithm has a better effectiveness against existing algorithms.