scholarly journals Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

2019 ◽  
Vol 11 (5) ◽  
pp. 559 ◽  
Author(s):  
Tianfang Zhang ◽  
Hao Wu ◽  
Yuhan Liu ◽  
Lingbing Peng ◽  
Chunping Yang ◽  
...  
Keyword(s):  
Convex Optimization ◽  
Target Detection ◽  
Detection Efficiency ◽  
Infrared Image ◽  
Low Rank ◽  
Small Target ◽  
Small Target Detection ◽  
Lp Norm ◽  
Norm Constraint ◽  
Infrared Small Target

The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.

scholarly journals Flying Small Target Detection for Anti-UAV Based on a Gaussian Mixture Model in a Compressive Sensing Domain

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2168 ◽  
Author(s):  
Chuanyun Wang ◽  
Tian Wang ◽  
Ershen Wang ◽  
Enyan Sun ◽  
Zhen Luo
Keyword(s):  
Compressive Sensing ◽  
Target Detection ◽  
Sparse Matrix ◽  
Infrared Image ◽  
Matrix Decomposition ◽  
Gaussian Mixture ◽  
Visual Surveillance ◽  
Low Rank ◽  
Small Target ◽  
Small Target Detection

Addressing the problems of visual surveillance for anti-UAV, a new flying small target detection method is proposed based on Gaussian mixture background modeling in a compressive sensing domain and low-rank and sparse matrix decomposition of local image. First of all, images captured by stationary visual sensors are broken into patches and the candidate patches which perhaps contain targets are identified by using a Gaussian mixture background model in a compressive sensing domain. Subsequently, the candidate patches within a finite time period are separated into background images and target images by low-rank and sparse matrix decomposition. Finally, flying small target detection is achieved over separated target images by threshold segmentation. The experiment results using visible and infrared image sequences of flying UAV demonstrate that the proposed methods have effective detection performance and outperform the baseline methods in precision and recall evaluation.

Infrared Small Target Detection Based on Morphology and SUSAN Algorithm

2019 ◽  
Vol 34 (06) ◽  
pp. 2050013 ◽  
Author(s):  
Zhiwei Hu ◽  
Yixin Su
Keyword(s):  
Target Detection ◽  
Infrared Image ◽  
Frequency Region ◽  
Guidance System ◽  
Background Suppression ◽  
Small Target ◽  
High Frequency Region ◽  
Small Target Detection ◽  
Small Targets ◽  
Infrared Small Target

Infrared small target detection is one of the key techniques in infrared imaging guidance system. The technology of infrared small target detection still needs to be further studied to improve the detection performance. This paper combines the high-pass filtering characteristics of morphological top-hat transform with SUSAN algorithm, and proposes a small infrared target detection method based on morphology and SUSAN algorithm. This method uses top-hat transform to detect the high-frequency region in infrared image, and filters out the low-frequency region in the image to implement the preliminary background suppression of infrared image. Then the SUSAN algorithm is used to detect small targets in the image after background suppression. The proposed method is applied to the single infrared image which is acquired by the infrared guidance system in the process of detecting and tracking the target under specific conditions. The experimental results show that the method is effective and can detect infrared small targets under different background.

scholarly journals Infrared Small Target Detection Based on Non-Overlapping Patch Model via l0-l1 Norm

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1426
Author(s):  
Jiaqi Yang ◽  
Yi Cui ◽  
Fei Song ◽  
Tao Lei
Keyword(s):  
Target Detection ◽  
Principal Component ◽  
Optimization Method ◽  
Low Rank ◽  
Computational Time ◽  
Background Suppression ◽  
Small Target ◽  
L1 Norm ◽  
Small Target Detection ◽  
Infrared Small Target

Infrared small target detection technology has sufficient applications in many engineering fields, such as infrared early warning, infrared tracking, and infrared reconnaissance. Due to the tiny size of the infrared small target and the lack of shape and texture information, existing methods often leave residuals or miss the target. To address these issues, a novel method based on a non-overlapping patch (NOP) joint l0-l1 norm is proposed with the introduction of sparsity regularized principal component pursuit (SRPCP). The NOP model makes the patch lighter in the first place, reducing time consumption. The adoption of the l0 norm enhances the sparsity of the target, while the adoption of the l1 norm enhances the robustness of the algorithm under clutter. As a smart optimization method, SRPCP solves the NOP model fittingly and achieves stable separation of low-rank and sparse components, thereby improving detection capacity while suppressing the background efficiently. The proposed method ultimately yielded favorable detection results. Adequate experiment results demonstrate that the proposed method is competitive in terms of background suppression and true target detection with respect to state-of-the-art methods. In addition, our method also reduces the computational time.

scholarly journals Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy

2020 ◽  
Vol 12 (9) ◽  
pp. 1520 ◽  
Author(s):  
Xuewei Guan ◽  
Landan Zhang ◽  
Suqi Huang ◽  
Zhenming Peng
Keyword(s):  
Target Detection ◽  
High Efficiency ◽  
Singular Value ◽  
Low Rank ◽  
Tensor Rank ◽  
Local Contrast ◽  
Small Target ◽  
Robust Detection ◽  
Small Target Detection ◽  
Infrared Small Target

Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-tensor (IPT) model, a non-convex tensor rank surrogate merging tensor nuclear norm (TNN) and the Laplace function, is utilized for low rank background patch-tensor constraint, which has a useful property of adaptively allocating weight for every singular value and can better approximate l 0 -norm. Considering that the local prior map can be equivalent to the saliency map, we introduce a local contrast energy feature into IPT detection framework to weight target tensor, which can efficiently suppress the background and preserve the target simultaneously. Besides, to remove the structured edges more thoroughly, we suggest an additional structured sparse regularization term using the l 1 , 1 , 2 -norm of third-order tensor. To solve the proposed model, a high-efficiency optimization way based on alternating direction method of multipliers with the fast computing of tensor singular value decomposition is designed. Finally, an adaptive threshold is utilized to extract real targets of the reconstructed target image. A series of experimental results show that the proposed method has robust detection performance and outperforms the other advanced methods.

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