Small infrared target detection utilizing Local Region Similarity Difference map

He, Qi; Mo, Bo; Liu, Fuxiang; He, Yuehan; Shengdong, Liu

doi:10.1016/j.infrared.2015.03.007

Cited by 22 publications

(6 citation statements)

References 13 publications

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“…Then, the task of target detection was formulated as a problem of separating the sparse and low rank matrices. To more fully exploit spatial correlationships, IPI model is further extended to tensor space and an innovative framework of separating background and target called infrared patch-tensor (IPT) model, which can be described as Equation (11), is proposed.…”

Section: Infrared Patch-tensor Modelmentioning

confidence: 99%

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

et al. 2020

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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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Section: Infrared Patch-tensor Modelmentioning

confidence: 99%

“…Moreover, various interferences, such as heavy cloud edges, sea clutters, and artificial heat source on the ground, usually cause high false alarm rates and weaken detection performance. Therefore, it is a very valuable and challenging work to study the small infrared target detection in the complex background [9,11].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…However, in general, targets are far away from the observation equipment, and infrared small targets occupy very few pixels in the image, appearing as patches or even dots, lacking effective shape features [1], as well as lacking texture, color, and shape features of common objects [2]. At the same time, in practical applications, sea surface scenes are complex and often contain static or slowly varying clutter, such as sea-sky lines, cloud clusters, and islands, as well as dynamic clutter, such as fish-scale light and sun glint.…”

Section: Introductionmentioning

confidence: 99%

An Infrared Maritime Small Target Detection Algorithm Based on Semantic, Detail, and Edge Multidimensional Information Fusion

Yao,

Xiao,

Deng

et al. 2023

Remote Sensing

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The infrared small target detection technology has a wide range of applications in maritime defense warning and maritime border reconnaissance, especially in the maritime and sky scenes for detecting potential terrorist attacks and monitoring maritime borders. However, due to the weak nature of infrared targets and the presence of background interferences such as wave reflections and islands in maritime scenes, targets are easily submerged in the background, making small infrared targets hard to detect. We propose the multidimensional information fusion network(MIFNet) that can learn more information from limited data and achieve more accurate target segmentation. The multidimensional information fusion module calculates semantic information through the attention mechanism and fuses it with detailed information and edge information, enabling the network to achieve more accurate target position detection and avoid detecting one target as multiple ones, especially in high-precision scenes such as maritime target detection, thus effectively improving the accuracy and reliability of detection. Moreover, experiments on our constructed dataset for small infrared targets in maritime scenes demonstrate that our algorithm has advantages over other state-of-the-art algorithms, with an IoU of 79.09%, nIoU of 79.43%, F1 score of 87.88%, and AuC of 95.96%.

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“…In recent years, infrared small target detection in the spatiotemporal domain has attracted much attention, as it has important significance for achieving early warning in the spatial domain, spatial surveillance, and so on [1,2]. However, due to the fact that small and weak targets are photoelectric signals for long-range imaging often accompanied by atmospheric turbulence and variable clouds, the targets are often submerged by these interferences [3][4][5][6], resulting in the target signal being too weak to be detected by the detector and ultimately leading to detection failure. Therefore, improving the detection efficiency of algorithm models and ensuring detection accuracy has become a key and difficult point in algorithm design research.…”

Section: Introductionmentioning

confidence: 99%

Dim and Small Target Detection Based on Energy Sensing of Local Multi-Directional Gradient Information

Fan

Min

et al. 2023

Remote Sensing

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It is difficult for traditional algorithms to remove cloud edge contours in multi-cloud scenarios. In order to improve the detection ability of dim and small targets in complex edge contour scenes, this paper proposes a new dim and small target detection algorithm based on local multi-directional gradient information energy perception. Herein, based on the information difference between the target area and the background area in the four direction neighborhood blocks, an energy enhancement model for multi-directional gray aggregation (EMDGA) is constructed to preliminarily enhance the target signal. Subsequently, a local multi-directional gradient reciprocal background suppression model (LMDGR) was constructed to model the background of the image. Furthermore, this paper proposes a multi-directional gradient scale segmentation model (MDGSS) to obtain candidate target points and then combines the proposed multi-frame energy-sensing (MFESD) detection algorithm to extract the true targets from sequence images. Finally, in order to better illustrate the effect of the algorithm proposed in this paper in detecting small targets in a cloudy background, four sequence images are selected for detection. The experimental results show that the proposed algorithm can effectively suppress the edge contour of complex clouds compared with the traditional algorithm. When the false alarm rate Pf is 0.005%, the detection rate Pd is greater than 95%.

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Small infrared target detection utilizing Local Region Similarity Difference map

Cited by 22 publications

References 13 publications

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

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

An Infrared Maritime Small Target Detection Algorithm Based on Semantic, Detail, and Edge Multidimensional Information Fusion

Dim and Small Target Detection Based on Energy Sensing of Local Multi-Directional Gradient Information

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