- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2,1 Norm
摘要： To improve the detection ability of infrared small targets in complex backgrounds, a novel method based on non-convex rank approximation minimization joint l2,1 norm (NRAM) was proposed. Due to the defects of the nuclear norm and l1 norm, the state-of-the-art infrared image-patch (IPI) model usually leaves background residuals in the target image. To fix this problem, a non-convex, tighter rank surrogate and weighted l1 norm are instead utilized, which can suppress the background better while preserving the target efficiently. Considering that many state-of-the-art methods are still unable to fully suppress sparse strong edges, the structured l2,1 norm was introduced to wipe out the strong residuals. Furthermore, with the help of exploiting the structured norm and tighter rank surrogate, the proposed model was more robust when facing various complex or blurry scenes. To solve this non-convex model, an efficient optimization algorithm based on alternating direction method of multipliers (ADMM) plus difference of convex (DC) programming was designed. Extensive experimental results illustrate that the proposed method not only shows superiority in background suppression and target enhancement, but also reduces the computational complexity compared with other baselines.
关键词： infrared image,structured norm,non-convex rank approximation minimization,small target detection
更新于2025-09-23 15:22:29
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Visual Detail Augmented Mapping for Small Aerial Target Detection
摘要： Moving target detection plays a primary and pivotal role in avionics visual analysis, which aims to completely and accurately detect moving objects from complex backgrounds. However, due to the relatively small sizes of targets in aerial video, many deep networks that achieve success in normal size object detection are usually accompanied by a high rate of false alarms and missed detections. To address this problem, we propose a novel visual detail augmented mapping approach for small aerial target detection. Concretely, we first present a multi-cue foreground segmentation algorithm including motion and grayscale information to extract potential regions. Then, based on the visual detail augmented mapping approach, the regions that might contain moving targets are magnified to multi-resolution to obtain detailed target information and rearranged into new foreground space for visual enhancement. Thus, original small targets are mapped to a more efficient foreground augmented map which is favorable for accurate detection. Finally, driven by the success of deep detection network, small moving targets can be well detected from aerial video. Experiments extensively demonstrate that the proposed method achieves success in small aerial target detection without changing the structure of the deep network. In addition, compared with the-state-of-art object detection algorithms, it performs favorably with high efficiency and robustness.
关键词： visual detail augmented mapping,small target detection,aerial video
更新于2025-09-23 15:22:29
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11259 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part IV) || Infrared Small Target Detection Using Multiscale Gray and Variance Difference
摘要： Infrared small target detection plays an important role in infrared monitoring and early warning systems. This paper proposes a local adaptive contrast measure for robust infrared small target detection using gray and variance di?erence. First, a size-adaptive gray-level target enhancement process is performed. Then, an improved multiscale variance di?erence method is proposed for target enhancement and cloud clutter removal. To demonstrate the e?ectiveness of the proposed approach, a test dataset consisting of two infrared image sequences with di?erent backgrounds was collected. Experiments on the test dataset demonstrate that the proposed infrared small target detection method can achieve better detection performance than the state-of-the-art approaches.
关键词： Small target detection,Infrared image,Local di?erence measures
更新于2025-09-23 15:21:01
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Current Trends in?Computer Science and?Mechanical Automation Vol.1 (Selected Papers from CSMA2016) || Image Small Target Detection based on Deep Learning with SNR Controlled Sample Generation
摘要： A small target detection method based on deep learning is proposed. First, random background parts are sampled from some cloud-sky images. Then, random generated target spots are added to the backgrounds with controlled signal to background noise ratio (SNR) to generate target samples. Then training and testing results show that the performance of deep nets is superior to tradition small target detection techniques and the selection of sampling SNRhas an important effect on nets training performances. SNR = 1 is a good selection for deep nets training, not onlyfor small target detection,but also for other applications.
关键词： SNR control,small target detection,Deep learning,Nerual Network
更新于2025-09-16 10:30:52
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Infrared small target detection based on non-subsampled shearlet transform and phase spectrum of quaternion Fourier transform
摘要： Infrared small target detection is a crucial part of infrared search and track system, and it has been a significant research topic in the past decades. Inspired by previous studies showing that phase spectrum of quaternion Fourier transform (PQFT) great superiority in salient region extraction and the desirable characteristics of multi-scale, multi-direction and shift-invariant with non-subsampled shearlet transform (NSST), a new target detection method is proposed based on NSST and PQFT in this paper. The original image is first subjected to NSST decomposition to obtain a low frequency component and four high frequency components by NSP. Next, directional localization is achieved by shearing filters which provides multi-directional decomposition. Then, four direction high frequency sub-images decomposed by NSST are introduced as four data channels of PQFT. The reconstruction map that highlights the salient region in the time domain is computed using the inverse PQFT. Lastly, the real target is directly segmented by an adaptive threshold. The proposed method is validated by five test sequences. The experimental results show that our method is superior to other traditional methods in terms of robustness and effectiveness in complex background.
关键词： Phase spectrum,Infrared small target detection,NSST,PFT,PQFT
更新于2025-09-16 10:30:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - An Effective Zoom-In Approach for Detecting DIM and Small Target Proposals in Satellite Imagery
摘要： Satellite high de?nition videos provide an opportunity to monitor moving targets over a large territory. However, the low spatial resolution and low contract of these videos make target detecting and tracking a challenging task. In this paper, we propose a zoom-in approach for detecting dim and small target proposals from each single frame of the videos to help with moving target tracking. Initialized by a coarse scale segmentation approach, dim and small targets are embedded in each superpixel due to limited size and weak signals. Similar superpixels are then merged using a graph-based approach based on the measurement of the overlap between their histograms. The background statistics becomes stronger and target pixels are more obvious in the merged superpixels, so that the target pixels can be extracted. Finally, the corresponding boundary box is generated for each spatially connected target pixels selected inside each superpixel. They form the dim and small target proposals. Experimental results show that our zoom-in scheme can generate less proposals with higher recall rate compared with state-of-the-art proposal extraction algorithms.
关键词： Target Proposal,Satellite High De?nition Video,Dim and Small Target Detection
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - An Adaptation of Cnn for Small Target Detection in the Infrared
摘要： Due to the low signal to noise ratio and limited spatial resolution, small target detection in an infrared image is a challenging task. Existing methods often have high false alarm rates and low probabilities of detection when infrared small targets submerge in the background clutter. In this paper, the Convolutional Neural Network (CNN) is adapted to extract the hidden features of small targets from infrared imagery with a proposed technique for a large amount of training data generation. The Point Spread Function (PSF) is employed to model the small target data and generate positive samples. The random background image patches are selected as the negative samples. In this way, the detection problem is skillfully converted into a problem of pattern classification using CNN. Extensive synthetic and real small targets were tested to evaluate the performance of this novel small target detection framework. The experimental results indicate that the proposed algorithm is simple and effective with satisfactory detection accuracy.
关键词： Infrared image (IR),Convolutional Neural Network (CNN),Point Spread Function (PSF),small target detection
更新于2025-09-10 09:29:36
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A Coarse-to-Fine Method for Infrared Small Target Detection
摘要： Infrared small target detection in a complex background is a challenging problem. A complex background generally contains structured edges, unstructured clutter, and noise, which completely have different properties. It is very difficult to separate small target from these interferences by exploiting one property. To solve this problem, we propose a coarse-to-fine method to gradually detect small target. In the coarse phase, nonlocal self-similarity property of the structured edges is exploited so as to separate the structured edges from the other components, such as the random noise, the unstructured clutter, and also the small target. In the fine phase, we utilize the local contrast prior of the small target in a local region so as to distinguish the small target from the unstructured clutter and noise. Multiscale information is further introduced to accommodate the changing size of the small target. This progressive detection pipeline utilizes the nonlocal, local, and multiscale information in a single image, which facilitates gradually differentiating the small target from the structured edges, unstructured clutter, and noise. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.
关键词： nonlocal self-similarity,infrared small target detection,Coarse to fine (CF),multiscale,local contrast
更新于2025-09-10 09:29:36
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Infrared Patch-tensor Model with Weighted Tensor Nuclear Norm for Small Target Detection in A Single Frame
摘要： The robust and ef?cient detection of infrared small target is a key technique for infrared search and track systems. Several robust principal component analysis (RPCA) based method have been developed recently, which have achieved state-of-the-art performance. However, there are still two drawbacks: 1) the false alarm ratio would raise under the heavy background clutters and noises, 2) these methods are usually time-consuming and not suitable for real-time processing. To solve this problem, an infrared patch-tensor model based on weighted tensor nuclear norm is proposed in this paper. First, the infrared image is transformed into the infrared patch-tensor (IPT). Considering the sum of nuclear norms (SNN) adopted in the IPT model is not the convex envelope of the tensor rank, and the solution is substantially suboptimal. The tensor nuclear norm is adopted to recover the underlying low-rank background tensor and spare target tensor, and the computation complexity can be reduced dramatically with the help of the tensor Singular Value Decomposition (t-SVD). Moreover, to further suppress the background clutters, a weight tensor is incorporated with tensor nuclear norm to preserve the background edges better. Then the separation between target and background is formulated as a convex weighted tensor RPCA (TRPCA) model. Finally, the proposed model can be solved by the Alternating Direction Method of Multipliers (ADMM). Extensive experiments demonstrate that the proposed model outperforms the other state-of-the-arts in performance and ef?ciency.
关键词： Infrared patch-tensor model,small target detection,weighted tensor robust principal component analysis
更新于2025-09-04 15:30:14