Suppress Background Clutter Research Articles

Infrared Small Target Detection Based on Density Peak Search and Local Features

The detection of small infrared targets is still a challenging task and efficient and accurate detection plays a key role in modern infrared search and tracking military applications. However, small infrared targets are difficult to detect due to their weak brightness, small size and lack of shape, structure, texture, and other information elements. In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high-brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high-brightness clutter. Second, a density-peaked global search method is used to determine the location of candidate targets in the preprocessed image. We then use local contrast to the candidate target points to enhance the gradient features and suppress background clutter. The Facet model is used to compute multidirectional gradient features at each point. A new efficient surrounding symmetric region partitioning scheme is constructed to capture the gradient characteristics of targets of different sizes in eight directions, followed by weighting the candidate target gradient characteristics using the standard deviation of the symmetric region difference. Finally, an adaptive threshold segmentation method is used to extract small targets. Experimental results show that the method proposed in this paper has better detection accuracy and robustness compared with other detection methods.

Semantically enhanced attention map‐driven occluded person re‐identification

AbstractOccluded person re‐identification (Re‐ID) is to identify a particular person when the person's body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. This paper proposes a novel attention map‐driven network (AMD‐Net) for occluded person Re‐ID. In AMD‐Net, human parsing labels are introduced to supervise the generation of partial attention maps, while a spatial‐frequency interaction module is suggested to complement the higher‐order semantic information from the frequency domain. Furthermore, a Taylor‐inspired feature filter for mitigating background disturbance and extracting fine‐grained features is proposed. Moreover, a part‐soft triplet loss, which is robust to non‐discriminative body partial features is also designed. Experimental results on Occluded‐Duke, Occluded‐Reid, Market‐1501, and Duke‐MTMC datasets show that this method outperforms existing state‐of‐the‐art methods. The code is available at: https://github.com/ISCLab‐Bistu/SA‐ReID.

Scheme design of the optimal background clutter suppression for impulse radio ultrawide band radar in complex fire environment: a simulation and experimental study

Impulse radio ultrawide band (IR-UWB) radar possesses several advantages, such as heat flow insensitivity, high resolution, great penetration, good mobility, and lightweight, making it a promising technology for detecting and rescuing individuals trapped in fire scenarios. However, reliable detection and extraction of vital signs of trapped people in the presence of complex fire environment, such as water droplets, smoke, wall occlusions, and reflections, require an optimal background clutter suppression scheme designed specifically for fire scenarios. This study comprehensively evaluates 20 background noise suppression schemes using an exhaustive approach that involves constructing radar echo simulation signals and designing an evaluation method for algorithms. The practical applicability of the improved design solutions was evaluated by conducting small-scale fire experiments in an acrylic chamber. The results indicate that the designed scheme is effective in overcoming the background interference from the combustion environment and is capable of supporting subsequent trajectory identification and vital sign extraction in the fire scene environment.

Joint spatio-temporal features and sea background prior for infrared dim and small target detection

The detection of dim and small targets in the complex sea background poses new challenges to automatic target search and interception by infrared air-to-air missiles at sea. To solve the problems of low detection rate of dim targets and massive suspected targets caused by the shadow region of clutter on the sea surface, this paper proposes a detection method for dim and small targets with spatio-temporal features and background prior information. Firstly, by combining the ideas of local energy factor and multi-scale patch contrast measure, an improved dim target detection method is proposed, which can effectively enhance the contrast of dim targets and suppress background clutter. Then, according to the grayscale probability distribution difference between the background and the target, a double-Gaussian model is established for the residual background and the target region and a background suppression method based on the spatial prior information is proposed to further suppress the residual background. Finally, a dim target detection method is proposed by combining the above dim target detection, background suppression, and dynamic pipeline filtering methods. The results indicate the average detection rate of the detection method proposed in this study is 91.44%. Also, the average number of false alarms in a single frame is reduced to 9.89, which is more than 80% lower than that of other algorithms.

Infrared Small Target Detection via Center-Surround Gray Difference Measure With Local Image Block Analysis

Exristing algorithms may suffer from high false alarm rate and low detection probability when detecting dim small target under intricate background clutters and heavy noise. To address this problem, a target detection method based on local image block analysis and center-surround gray difference measure (CGDM) is proposed in this article. First, the infrared image is decomposed into a group of local blocks. The gray information of each block are sorted in ascending order and the last element is deleted, then the maximum gray jump point (MGJP) is extracted by using differential operation. Second, a protection zone is formed with MGJP as the center, and the maximal intensity of protection zone (MIPZ) is extracted. Next, by comparing the gray intensity of MGJP and MIPZ values, the potential target blocks can be reliably selected, while most background blocks and noise blocks will be discarded. After that, the CGDM is presented to enhance the target and suppress background clutters, and then, the center-surround local contrast measure (CLCM) is designed to further suppress the high-intensity clutter residues by searching the target center and revising the protection zone. Finally, the weighted center-surround gray difference measure (WCGDM) is defined by CLCM WCGDM map to recognize real targets. Extensive experiments show that the proposed method outperforms several existing algorithms in small target detection under complex background clutters, and it is robust to various target shapes, target sizes, and noise types.

Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

In order to solve the problem of low positioning accuracy of mechanical fault diagnosis, a polarization GPR imaging reconstruction algorithm based on the MMV model was proposed. The algorithm was mainly based on the joint processing of the measured data of multiple polarization channels to achieve the reconstruction of the reflectance of the detection scene corresponding to each polarization channel. The simulation data processing results based on FDTD showed that compared with the traditional SMV model polarization imaging algorithm, the proposed imaging algorithm could improve the accuracy of target location reconstruction and the ability of background clutter suppression significantly. Compared with the SMV model, TCR obtained by the MMV model increased by 30%. As for the imaging results at different noise ratios, TCR obtained by the MMV model was 10% higher than that obtained by the SMV model. And when the ratio of available real data samples decreased to 25%, the sample data generation based on the adversarial generation network could greatly improve the classification accuracy of the fault diagnosis model. It could realize the detection of the target better, so as to locate faults accurately.

Inshore Ship Detection in Large-Scale SAR Images Based on Saliency Enhancement and Bhattacharyya-like Distance

While the detection of offshore ships in synthetic aperture radar (SAR) images has been widely studied, inshore ship detection remains a challenging task. Due to the influence of speckle noise and the high similarity between onshore buildings and inshore ships, the traditional methods are unable to achieve effective detection for inshore ships. To improve the detection performance of inshore ships, we propose a novel saliency enhancement algorithm based on the difference of anisotropic pyramid (DoAP). Considering the limitations of IoU in small-target detection, we design a detection framework based on the proposed Bhattacharyya-like distance (BLD). First, the anisotropic pyramid of the SAR image is constructed by a bilateral filter (BF). Then, the differences between the finest two scales and the coarsest two scales are used to generate the saliency map, which can be used to enhance ship pixels and suppress background clutter. Finally, the BLD is used to replace IoU in label assignment and non-maximum suppression to overcome the limitations of IoU for small-target detection. We embed the DoAP into the BLD-based detection framework to detect inshore ships in large-scale SAR images. The experimental results on the LS-SSDD-v1.0 dataset indicate that the proposed method outperforms the basic state-of-the-art detection methods.

Infrared small target detection using kernel low-rank approximation and regularization terms for constraints

The robust detection of infrared (IR) small targets remains a challenging task. When targets are submerged in heavy clutter or disturbed by strong edge, it is difficult to extract targets and suppress background clutter simultaneously. In this paper, we present a kernel robust principal component analysis model for IR small target detection. Firstly, to overcome the interference of heavy clutter and strong edge, the kernel low-rank approximation is proposed to estimate the background component; Then, to further improve detection performance, the lp-norm is adopted to recover the sparse component, the total variation regularization is employed to smooth the background, and the graph Laplacian regularization is utilized as a sparse constraint to constrain the homogeneous target region. Finally, we derive an effective method to solve the proposed model based on the alternating direction method of multipliers. Experimental results not only show the proposed method can robustly and effectively detect IR small targets even when the targets are submerged in strong clutter or seriously disturbed by strong edge, but also confirm the proposed method is competitive with the state-of-the-art methods.

Infrared Small-Target Detection Using Multiscale Local Average Gray Difference Measure

In infrared (IR) guidance and target tracking systems, dim target intensity and complex background clutter are some of the typical challenges, especially for the accurate detection of small objects. In this article, we propose a novel IR target detection method based on new local contrast measures. First, the local average gray difference measure (LAGDM) is presented to accentuate the difference between a small object and its local background. Then, an LAGDM map is generated to effectively enhance targets and suppress background clutter. Finally, we use an adaptive segmentation method to separate the object from the background. Experimental results on multiple sequences show that the proposed small-target detection method can effectively improve the signal-to-clutter ratio (SCR) of the image, and it exhibits robust performance against cloudy sky, sea sky, and mountain forest backgrounds.

A Lightweight Position-Enhanced Anchor-Free Algorithm for SAR Ship Detection

As an active microwave device, synthetic aperture radar (SAR) uses the backscatter of objects for imaging. SAR image ship targets are characterized by unclear contour information, a complex background and strong scattering. Existing deep learning detection algorithms derived from anchor-based methods mostly rely on expert experience to set a series of hyperparameters, and it is difficult to characterize the unique characteristics of SAR image ship targets, which greatly limits detection accuracy and speed. Therefore, this paper proposes a new lightweight position-enhanced anchor-free SAR ship detection algorithm called LPEDet. First, to resolve unclear SAR target contours and multiscale performance problems, we used YOLOX as the benchmark framework and redesigned the lightweight multiscale backbone, called NLCNet, which balances detection speed and accuracy. Second, for the strong scattering characteristics of the SAR target, we designed a new position-enhanced attention strategy, which suppresses background clutter by adding position information to the channel attention that highlights the target information to more accurately identify and locate the target. The experimental results for two large-scale SAR target detection datasets, SSDD and HRSID, show that our method achieves a higher detection accuracy and a faster detection speed than state-of-the-art SAR target detection methods.

RGBT tracking based on cooperative low-rank graph model

The existing graph-based RGBT tracking methods mainly focus on assigning a weight to each local image patch to suppress background influence in target bounding box, but the influences of background clutter might limit the improvement of tracking performance. To solve this problem, we propose a new algorithm, called cooperative low-rank graph model, to suppress background clutter. Specifically, the proposed feature decomposition module decomposes input dual-modal features into low-rank components and sparse noisy components, which could be used collaboratively by regularizing graph learning by combining modal weights. Besides, to avoid SVD (Singular Value Decomposition) operations we have designed an efficient solver based on ADMM (Alternating Direction Methods of Multipliers), which could factorize the low-rank matrix into two low-dimensional submatrices. Extensive experiments on four RGBT tracking benchmark data sets show that our method performs favorably against other state-of-the-art tracking algorithms, and achieves more robust tracking performance.

Region proposal patch-image model for infrared small target detection

ABSTRACT The infrared patch-image (IPI) model has been proved to be very successful in infrared small target detection. However, the IPI model suffers from two major issues: abundant of residuals in the recovered target image and low detection speed caused by the large-size low-rank decomposition matrix. To mitigate these challenges, a novel infrared small target detection method called the region proposal patch-image (RPPI) model is proposed. The method contains two stages, where the first one is to generate region proposals (RPs) based on the contrast mechanism and the second one is to recover targets based on matrix decomposition. In the first stage, RPs containing interest targets are extracted in order to suppress background clutters and reduce the size of the decomposed matrix. In RP extraction, the contrast mechanism instead of the original convolutional neural network (CNN) is adapted due to lack of texture features for infrared small target and high computational complexity with CNN. In the second stage, considering the weakness of the correlation between RPs, a weighted nuclear norm (WNN) is utilized for avoiding relaxation of the sparsity too much, which can preserve targets and suppress the background simultaneously. Finally, accelerated proximal gradient (APG)-combined WNN is applied to solve this model. Experimental results on eight image groups with various kinds of targets and backgrounds demonstrate that the proposed method can effectively detect the target with faster speed, higher detection rate, and lower false alarm rate compared with other state-of-the-art methods.

Classification of Improvised Explosive Devices Using Multilevel Projective Dictionary Learning With Low-Rank Prior

Improvised explosive devices (IEDs) pose a significant threat to defense forces and humanitarian demining personnel. They are weapons of modern times, made from nonconventional military materials, rendering them difficult to identify when buried in the ground. Numerous studies focus on detecting these explosive threats and reducing the false alarm rate. However, there are few attempts to identify the detected explosive devices to take proper countermeasures. This article presents a multilevel projective dictionary learning (DL) method to classify ground-penetrating radar signals from IEDs. The proposed dictionary learning method solves three different tasks simultaneously: suppressing background clutter, learning a set of discriminative features for classification, and training a classifier. The suppression of ground clutter is formulated as a low-rank (LR) optimization problem with sparse constraints, where a low-rank subspace is learned from background clutter signals. Dictionary learning is used to transform the target signals into discriminative feature vectors, which are in turn used by the classifier to predict the target class. Experiments were conducted on real radar data. The results showed that the proposed method is more effective than the existing dictionary models and machine learning methods.

Infrared Small Target Detection Based on Gradient-Intensity Joint Saliency Measure

Small target detection is an arduous mission in infrared search and tracking (IRST) system, especially when the target signal is disturbed by high-intensity background clutters. In view of this situation, this paper presents a robust target detection algorithm based on gradient-intensity joint saliency measure (GISM) to gradually eliminate complex background clutter. Because of thermal remote sensing imaging, the infrared target usually occupies a small area that accords with the optics point spread function (PSF), so it can be distinguished from the background clutter in both gradient and intensity properties. According to this, firstly, the original image is transformed into a gradient map, and the gradient saliency measure (GSM) is calculated to highlight the target signal and suppress the sharp edge clutter, so the candidate targets can be reliably extracted by using the maximum entropy principle. Secondly, the local intensity saliency measure (LISM) is obtained by calculating the gray difference between each candidate region and its local surroundings, so as to preserve the real target and remove intense structural clutter such as black holes or corners. Finally, by fully integrating the gradient and intensity properties, the GISM defined by LISM weighted GSM map can efficiently identify the real target signal and eliminate false alarms. Experimental results prove that the proposed method not only has advantages in background clutter suppression and small target enhancement, but also has reasonable time consumption.

A Novel 4D Track-before-Detect Approach for Weak Targets Detection in Clutter Regions

A 4D TBD approach is developed here for closely weak extended target tracking and overcoming heterogeneous clutter background and various clutter regions. The 4D measurements in this work are the points containing three positional information in spatial space and corresponding timestamp. The proposed method is mainly designed to address two issues. The first one is the dilemma between the weak target detection and difficult computation originating from the high dimensions of measurement. The second issue is the suppression of inhomogeneous background clutter and various clutter regions. The extension experiment using synthetic data showcases that no false alarm track would be built in the clutter regions, and the detection rate of close targets exceeds 94%. The experiments using real 3D radar also prove that the method works well in tracking closely maneuvering extended targets even if a clutter region exists.

An infrared dim target detection algorithm based on density peak search and region consistency

To suppress background clutter and improve detection accuracy, we propose a dim target detection algorithm based on density peak search and region consistency. A density peak search algorithm is first applied to extract candidate targets, and these are then classified and marked according to the local mosaic probability factor, which is important in order to suppress the backgroundsssss clutter and accurately strip the candidate target region from the background. Based on the regional stability of the dim targets, local mosaic gradient factors are used to screen real targets from candidates, and a facet kernel filter is used to extract the irregular contours of dim targets with the aim of enhancing them. Our experimental results show that compared with existing algorithms, the proposed method has better detection accuracy and robustness in various complex scenarios.

Adaptive Background Clutter Mitigation for Millimeter Wave MIMO Imaging

To suppress background clutter in short-range millimeter wave multiple-input multiple-output (MIMO) imaging, an adaptive and cost-efficient approach is proposed based on the availability of the background observation. By analyzing the origins of the background clutter, gain variation, and blocking effect are identified as the primary culprits responsible for the background clutter variation, which are treated separately in the proposed method. The gain variation is compensated with an adaptive power weighting (APW) procedure, where the gain variance is accurately and rapidly measured using the short-range profile segment associated with the interelement direct coupling. The clutter residual due to the blocking effect after APW is further removed by the combination of two zero-thresholding processes and coherence-factor-based image enhancement. The proposed method entails relatively light computational burden at the cost of acceptable memory increment, and is, thus, more amenable to real-time imaging requirement. Both simulations and experimental imaging tests on an MIMO array demonstrate the effectiveness and cost-efficiency of the proposed approach.

Infrared small target detection based on robust principal component analysis joint directional derivative penalty

Many robust principal component analysis based methods have been proposed for infrared small target detection recently. However, due to the lack of local prior information, these methods show poor performance under complex backgrounds. In this paper, a novel infrared patch-image model joint the directional derivative saliency feature is proposed. First, Laplace norm minimization is used instead of nuclear norm minimization to approximate low rank background can automatically capture the inherent rank information, which has higher matching with the real rank. Then, considering that the l 1-norm constrains target patch-image inexactly, we construct a saliency map by using the directional derivative feature of the target and incorporate it into the l 1-norm to enhance target and suppress background clutter simultaneously. Extensive experiments show the superiority of the proposed method to the other state-of-the-art methods in robustness and efficiency.

制冷型中波红外偏振成像光学系统设计

In addition to the advantages of traditional infrared intensity imaging, such as long detection distance, all-weather working and good concealment, infrared polarization imaging technology based on the difference between the target and background polarization characteristics can effectively reduce background interference, suppress background clutter, enhance image contrast, improve signal-to-noise ratio, and has broad application prospects. In order to effectively suppress the background clutter interference in the process of air and sea detection, enhance the target detection ability in fog, haze and smoke, as well as the backgrounds of small temperature differences and low illumination, the design of a four channel aperture sharing medium-wave infrared polarization imaging optical system with a focal length of 240 mm was completed. Tolerance analysis was carried out by using Monte Carlo method to ensure the rationality of the precision of optical system processing and assembly. The results of image quality analysis show that the MTF of the optical system is close to the diffraction limit, aberrations have been effectively corrected, and the imaging quality is good. The influence of Narcissus effect on the imaging quality of the cooled infrared optical system was verified by Narcissus analysis. In addition, the optical system proposed has a compact structure and high transmittance, avoiding the use of aspheric surfaces, and has good processing and assembly technique.

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.