Dim Small Target Research Articles

Single-Pixel Background Modeling Algorithm for Strong Sky Scenes Based on Local Region Spatial Bases.

In the dim-small target detection field, background suppression is a key technique for stably extracting the target. In order to effectively suppress the background to enhance the target, this paper presents a novel background modeling algorithm, which constructs base functions for each pixel based on the local region background and models the background of each pixel, named single pixel background modeling (SPB). In SPB, the low-rank blocks of the local backgrounds are first obtained to construct the background base functions of the center pixel. Then, the background of the center pixel is optimally estimated by the background bases. Experiments demonstrate that in the case of extremely low signal-to-noise ratio (SNR < 1.5 dB) and complex motion state of targets, SPB can stably and effectively separate the target from the strongly undulant sky background. The difference image obtained via SPB background modeling has the characters: the non-target residual could be white noise, and the target is significantly enhanced. Compared with the other typical five algorithms, SPB remarkably outperforms other algorithms to detect the target of a low signal-to-noise ratio.

Detection of Dim Small Ground Targets in SAR Remote Sensing Image based on Multi-level Feature Fusion

Detection of dim small ground targets in SAR remote sensing images suffers from deficient target information and significant irrelevant background noise. In order to solve this problem, we propose the CD-YOLO (Cross-DRSN YOLO) based on multi-level feature fusion. In CD-YOLO, the convolutional neural network is firstly used to extract features from the input SAR image step by step, thus the spatial pyramid of shallow and deep feature maps is obtained. Cross-level feature fusion is then performed on the spatial pyramid of feature maps, combined with the constructed soft thresholding module which adopts DRSN (Deep Residual Shrinkage Network), to enhance the spatial features of dim small targets and eliminate the noise-related features. Finally, end-to-end target detection is carried out on the two large parallel feature maps generated after soft thresholding. Detection results are output combined with multi-channel information. Due to lack of sufficient image data, a SAR dim small ground target dataset named SGDSTD (SAR Small Ground-based Dim Small Target Dataset) is constructed. Experimental results show that CD-YOLO achieves a real-time performance of 32.3 frames per second, and its AP0.5 and AP0.5: 0.95 on the SGDSTD dataset achieve 91.6% and 51.9%, respectively. Precision and recall of CD-YOLO on the SGDSTD dataset are 86.9% and 81.4%, respectively. The experiment results prove the effectiveness of the proposed method.

DMEF-Net: Lightweight Infrared Dim Small Target Detection Network for Limited Samples

DMEF-Net: Lightweight Infrared Dim Small Target Detection Network for Limited Samples

Light Dim Small Target Detection Network with Multi-Heterogeneous Filters

Light Dim Small Target Detection Network with Multi-Heterogeneous Filters

Receptive-Field and Direction Induced Attention Network for Infrared Dim Small Target Detection With a Large-Scale Dataset IRDST

Infrared small target detection plays an important role in military and civilian fields while it is difficult to be solved by deep learning (DL) technologies due to scarcity of data and strong interclass imbalance. To relieve scarcity of data, we build a massive dataset IRDST, which contains 142 727 frames. Also, we propose a receptive-field and direction-induced attention network (RDIAN), which is designed using the characteristics of target size and grayscale to solve the interclass imbalance between targets and background. Using convolutional layers with different receptive fields in feature extraction, target features in different local regions are captured, which enhances the diversity of target features. Using multidirection guided attention mechanism, targets are enhanced in low-level feature maps. Experimental results with comparison methods and ablation study demonstrate effective detection performance of our model. Dataset and code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://xzbai.buaa.edu.cn/datasets.html</uri> .

RLPGB-Net: Reinforcement Learning of Feature Fusion and Global Context Boundary Attention for Infrared Dim Small Target Detection

RLPGB-Net: Reinforcement Learning of Feature Fusion and Global Context Boundary Attention for Infrared Dim Small Target Detection

Infrared Dim Small Target Detection Method Based on Enhanced Local Contrast

Infrared Dim Small Target Detection Method Based on Enhanced Local Contrast

Hierarchical convolution filtering method for infrared dim small target detection

Hierarchical convolution filtering method for infrared dim small target detection

Infrared Small Dim Target Detection Under Maritime Near Sea–Sky Line Based on Regional-Division Local Contrast Measure

Infrared Small Dim Target Detection Under Maritime Near Sea–Sky Line Based on Regional-Division Local Contrast Measure

Infrared dim-small target detection under complex background based on attention mechanism

Infrared dim-small target detection under complex background based on attention mechanism

Local Contrast Attention Guide Network for Detecting Infrared Small Targets

In the process of downsampling, the detection of small infrared targets encounters problems such as imaging area, missing texture features, and disappearance of targets. This study proposes a local contrast attention guide network (LCAGNet), which implements and uses a SPD-CSP Resblock (i.e., CSPResblock based on sub-pixel DownSample), a MALC (i.e., multiple attention local contrast) module, and MCAF (i.e., multi-scale cooperative attention fusion) module, as well as e-maxmin (i.e., natural logarithm normalization) loss function for reconstructing images. First, by segmenting the gradient flow, the SPD-CSP Resblock propagates it in different paths. Second, under the guidance of the MALC module, the accuracy and convergence speed of the network are improved by constructing local nonlinear feature layers. Third, the weighted low-level detail feature channel is integrated with the deep semantic feature by MCAF to enhance the semantic information of the target. By standardizing the NL(i.e., natural logarithm) probability of the network output, the loss of target shape and position could be calculated accurately. Also, in the upsampling stage, the sub-pixel conv method reconstructs features with high resolution and further boosts detection accuracy. A series of detailed ablation experiments were conducted on each module in the network architecture to verify the effectiveness of the module and compare LCAGNet with other advanced data- and model-driven methods. The experimental results showed that LCAGNet achieved better results than other advanced benchmarking methods on two datasets of infrared small-dim target detection and tracking in both single-frame infrared small target benchmark (SIRST) and ground-sky background (KBT).

An Intelligent Particle Filter for Infrared Dim Small Target Detection and Tracking

With consideration of low tracking accuracy and even losing target when the track-before-detect method based on particle filter (PF-TBD) tracks infrared dim small target in the complex background. In this article, a track-before-detect method based on the spring model firefly algorithm optimization particle filter (SFA-PF-TBD) was proposed to address this problem. First, the attractiveness and movement behavior of fireflies were introduced into the particle filter for optimizing the particles, and the optimization strength was controlled by evaluating the real-time distribution of particles. After optimization, detecting the density of the particles around the optimal particle, the elastic mechanism of spring was used to control the density of particles around the optimal particle when the particles gathered excessively, which made the distribution of particles more reasonable. Then, the TBD method was realized by the improved particle filter for tracking dim small target under the low signal-noise ratio conditions. Finally, we compared the SFA-PF-TBD algorithm with other algorithms by tracking experiments in simulation scenes and actual scenes. The results showed that the SFA-PF-TBD algorithm has more advantages than the PF-TBD algorithm.

Infrared Fast-Maneuvering Target Tracking Based on Robust Exact Differentiator with Improved Particle Filter

This paper deals with the problem of nonlinear uncertainties when tracking an infrared dim small target with fast maneuvers. The particle filter (PF)-based methods are mostly considered. The existing improvement methods for the PF can handle the infrared dim small target with the conventional maneuver, the motion state of which changes slowly and in most cases is assumed to be linear. However, when fast maneuvers appear on the target, the PF-based method will soon suffer from particle degeneracy, or even loss of the target. In this paper, a robust exact differentiator (RED)-based particle generating method is proposed to improve the PF. New birth particles are produced by the proposed method, which can keep up with the fast maneuvers, and ensure the particle diversity of the PF, so as to avoid the particle degeneracy and depletion, meanwhile the number of particles required can be decreased. Numerical simulations are conducted, showing that the proposed algorithm has more advantages than the state-of-the-art method.

Nighttime infrared ship target detection based on Two-channel image separation combined with saliency mapping of local grayscale dynamic range

The automatic detection of infrared ship targets at night is the key research of intelligent maritime monitoring. Because the infrared maritime small targets are always dim, owning only simple texture, and variable in shape in voyage, deep learning is not so suitable for these kinds of target detection. In this paper, two-channel image separation combined with saliency mapping of local grayscale dynamic range (TCS-SMoLGDR) algorithm for ship target detection is proposed. The saliency mapping of local grayscale dynamic range (SMoLGDR) is proposed by taking full advantage of the feature that the infrared ship target at night owns the largest grayscale dynamic range. A saliency map is generated through SMoLGDR, in which the target area is significantly enhanced and the background is effectively suppressed. On the basis of the saliency map, the connected domain mean strategy is used to determine the real target area, which is beneficial to retain the complete target. For better segmentation of the target with uneven grayscale distribution from the background, this paper proposes a two-channel image separation (TCS) method to separate the local image of the target area into a bright channel image and a dark channel image, so that the grayscale distribution of targets in sub-images becomes relatively uniform. Finally, edge-guided binarization is used to extract the target. The experiment results show fully that the algorithm proposed in this paper can achieve the effective detection of the small dim targets in the nighttime infrared maritime image, and the detection accuracy is better than the comparison algorithms.

Noise Parameter Estimation Two-Stage Network for Single Infrared Dim Small Target Image Destriping

The existing nonuniformity correction methods generally have the defects of image blur, artifacts, image over-smoothing, and nonuniform residuals. It is difficult for these methods to meet the requirements of image enhancement in various complex application scenarios. In particular, when these methods are applied to dim small target images, they may remove dim small targets as noise points due to the image over-smoothing. This paper draws on the idea of a residual network and proposes a two-stage learning network based on the imaging mechanism of an infrared line-scan system. We adopt a multi-scale feature extraction unit and design a gain correction sub-network and an offset correction sub-network, respectively. Then, we pre-train the two sub-networks independently. Finally, we cascade the two sub-networks into a two-stage network and train it. The experimental results show that the PSNR gain of our method can reach more than 15 dB, and it can achieve excellent performance in different backgrounds and different intensities of nonuniform noise. Moreover, our method can avoid losing texture details or dim small targets after effectively removing nonuniform noise.

Small and dim infrared moving target detection based on spatial–temporal saliency

Infrared target detection is a significant work in military, high accuracy and high speed are the essential capabilities for precise guidance. With limited conditions, making full use of spatial–temporal information can really improve performance. This article studied small and dim infrared moving target detection based on sequence images. Quaternion Fourier Transform is an efficient method of visual saliency. To make this algorithm more accurate, we introduced four preprocessing feature map as input channels for quaternion. They are selective inter frame difference, fuzzy-based morphology filter, joint local gradient and gray contrast measure and temporal-constrained Gaussian curvature filter. All of them aim to highlight small-dim target and help to construct saliency map. Simultaneously, a trajectory constraint is introduced to reduce false alarm rate. Compared with single-frame based methods and multi-frames based methods, our novel algorithm performed better on balancing time consuming and detection precision.

Total Variation Weighted Low-Rank Constraint for Infrared Dim Small Target Detection

Infrared dim small target detection is the critical technology in the situational awareness field currently. The detection algorithm of the infrared patch image (IPI) model combined with the total variation term is a recent research hotspot in this field, but there is an obvious staircase effect in target detection, which reduces the detection accuracy to some extent. This paper further investigates the problem of accurate detection of infrared dim small targets and a novel method based on total variation weighted low-rank constraint (TVWLR) is proposed. According to the overlapping edge information of image background structure characteristics, the weights of constraint low-rank items are adaptively determined to effectively suppress the staircase effect and enhance the details. Moreover, an optimization algorithm combined with the augmented Lagrange multiplier method is proposed to solve the established TVWLR model. Finally, the experimental results of multiple sequence images indicate that the proposed algorithm has obvious improvements in detection accuracy, including receiver operating characteristic (ROC) curve, background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG). Furthermore, the proposed method has stronger robustness under complex background conditions such as buildings and trees.

Research on infrared dim and small target detection algorithm based on frequency domain difference

ABSTRACT The high accuracy of infrared dim and small target detection in complex backgrounds is of great relevance for infrared identification and tracking systems. Traditional infrared dim and small target detection methods suit scenes with a single and homogeneous continuous background. However, human vision system methods suffer from an undetectable or high false alarm rate in complex scenes with dim small targets. To address this shortcoming, this paper proposes an infrared dim and small target detection algorithm based on frequency domain differencing (FDD). The proposed algorithm consists of a spectrum residual module and a Gaussian greyscale difference module. Firstly, the target enhancement image is constructed by using the spectrum residual module to highlight small targets and suppress background noise. Secondly, the local contrast of the image is enhanced by the Gaussian grayscale difference module, which accurately depicts the edge information of small targets and locates them. Finally, the target enhancement image and Gaussian grayscale difference image are fused to detect infrared dim and small targets. Experimental results show that the proposed algorithm has higher accuracy under the evaluation metrics of local signal-to-noise ratio gain (LSNRG), signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF). At the same time, compared with other algorithms, the detection rate of the proposed algorithm is higher for infrared dim and small targets in complex scenes. Code is available at https://github.com/m156879/FDD-module.

Infrared small target energy distribution modeling for 2D subpixel motion and target energy compensation detection

Infrared (IR) dim small target detection in heavy noise has always been a challenging research task. Detection methods based on energy accumulation have achieved effective detection results. However, the general inaccuracy of the target energy distribution model for subpixel motion weakens the performance of these detection methods. The discretization process of a target is described in detail, and the effect of two-dimensional (2D) subpixel motion on the energy spatial distribution of the target is introduced. Based on the analysis of target energy distribution for subpixel motion, a novel energy spatial distribution model of the target for 2D subpixel motion (NESDSM) is proposed. The NESDSM model well describes the energy spatial distribution variation of a subpixel moving target. Furthermore, to verify the accuracy of the model, a target detection method based on energy compensation accumulation (ECA) is proposed. The ECA synchronously enhances the signal noise ratio and suppresses noise effectively. Finally, experimental results show that ECA has better detection performance in comparison with traditional energy accumulation methods. Moreover, by analyzing the performance of ECA in a quantitative manner, ECA shows an average 10.5% improvement in the output SNR in comparison with energy accumulation methods.

Detecting Dim Small Target in Infrared Images via Subpixel Sampling Cuneate Network

Infrared dim small target detection is regarded as a critical technology for the interpretation of space-based remote sensing images. In recent years, driven by deep learning technology and the surge of data, remarkable effects have been achieved for dim small target detection in infrared images. Nevertheless, the intrinsic feature scarcity and low signal-to-clutter ratio (SCR) characteristics pose tremendous challenges to deep learning-based detection methods. In this letter, we present a novel sub-pixel sampling cuneate network (SPSCNet) to detect dim small targets in infrared images. The overall model architecture is based on an end-to-end cuneate network with multiple groups of parallel high-to-low resolution subnetworks. Specifically, we design a multi-scale feature reweighted fusion (MSFRF) module to effectively fuse multi-scale feature maps which contain both low-level detail features and high-level semantics information. In addition, considering that the pooling operation may lose dim small targets with low SCR, we also exploit a sub-pixel sampling scheme to greatly retain the features of small targets. Moreover, to better test and verify the performance of the proposed method, we also develop an infrared dim small target (IDST) dataset to conduct more comparative experiments. Extensive experiments on the SIRST and IDST datasets illustrate that the proposed SPSCNet yields state-of-the-art performance in comparison with other detection algorithms.