Infrared small target detection based on the tensor model

2014 ◽  
Author(s):  
Jie Cao ◽  
Chenqiang Gao ◽  
Yongxing Xiao ◽  
Pei Li ◽  
Minglei Cai
Keyword(s):  
Target Detection ◽  
Tensor Model ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

scholarly journals Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

2019 ◽  
Vol 11 (4) ◽  
pp. 382 ◽  
Author(s):  
Landan Zhang ◽  
Zhenming Peng
Keyword(s):  
Real Time ◽  
Target Detection ◽  
State Of The Art ◽  
Nuclear Norm ◽  
Tensor Model ◽  
Small Target ◽  
Small Target Detection ◽  
Partial Sum ◽  
Tensor Nuclear Norm ◽  
Infrared Small Target

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

Infrared small target detection based on an image-patch tensor model

2019 ◽  
Vol 99 ◽  
pp. 55-63 ◽  
Author(s):  
Xiangyue Zhang ◽  
Qinghai Ding ◽  
Haibo Luo ◽  
Bin Hui ◽  
Zheng Chang ◽  
...  
Keyword(s):  
Target Detection ◽  
Tensor Model ◽  
Small Target ◽  
Image Patch ◽  
Small Target Detection ◽  
Infrared Small Target

scholarly journals Infrared small target detection based on region proposal and CNN classifier

2021 ◽  
Author(s):  
Mingming Fan ◽  
Shaoqing Tian ◽  
Kai Liu ◽  
Jiaxin Zhao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Rate ◽  
Corner Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Potential Target ◽  
Infrared Small Target

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

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