Infrared small target detection with complex background based on image layer and confidence analysis

2015 ◽  
Author(s):  
Hang Li ◽  
Qi Zhang ◽  
Yuanyuan Li ◽  
Liqiang Wang
Keyword(s):  
Target Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Image Layer ◽  
Complex Background ◽  
Infrared Small Target

scholarly journals Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 755
Author(s):  
He Wang ◽  
Yunhong Xin
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Saliency Map ◽  
Adaptive Threshold ◽  
Contourlet Transform ◽  
Small Target ◽  
Small Target Detection ◽  
Complex Background ◽  
Infrared Small Target

Wavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background.

Infrared Small Target Detection under Complex Background

2011 ◽  
Vol 346 ◽  
pp. 615-619 ◽  
Author(s):  
Gui Hua Peng ◽  
He Chen ◽  
Qiang Wu
Keyword(s):  
Image Segmentation ◽  
Target Detection ◽  
Information Entropy ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Small Target ◽  
Local Energy ◽  
Small Target Detection ◽  
Complex Background ◽  
Infrared Small Target

This paper presents an algorithm for detecting the small infrared target under complex background. An method, Local Mutation Weighted Information Entropy (LMWIE), is proposed to suppress background. Then, enhance targets’ gray value by calculating the local energy. For the problem that the gray value of noises is enhanced with the gray value improvement of targets, image segmentation bases on the adaptive threshold. Experiment results indicate that it is a robust and effective small target detection algorithm.

scholarly journals Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Houzhang Fang ◽  
Min Chen ◽  
Xiyang Liu ◽  
Shoukui Yao
Keyword(s):  
Total Variation ◽  
Target Detection ◽  
Objective Measure ◽  
Real Data ◽  
Small Target ◽  
Target Image ◽  
Small Target Detection ◽  
Complex Background ◽  
Patch Image ◽  
Infrared Small Target

Infrared small target detection plays an important role in infrared search and tracking systems applications. It is difficult to perform target detection when only a single image with complex background clutters and noise is available, where the key is to suppress the complex background clutters and noise while enhancing the small target. In this paper, we propose a novel model for separating the background from the small target based on nonlocal self-similarity for infrared patch-image. A total variation-based regularization term for the small target image is incorporated into the model to suppress the residual background clutters and noise while enhancing the smoothness of the solution. Furthermore, a reweighted sparse constraint is imposed for the small target image to remove the nontarget points while better highlighting the small target. For higher computational efficiency, an adapted version of the alternating direction method of multipliers is employed to solve the resulting minimization problem. Comparative experiments with synthetic and real data demonstrate that the proposed method is superior in detection performance to the state-of-the-art methods in terms of both objective measure and visual quality.

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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