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.

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.

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 Method Combined with Bilateral Filter and Local Entropy

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhonghua Wang ◽  
Siwei Duan ◽  
Chengli Sun
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Bilateral Filter ◽  
Background Suppression ◽  
Small Target ◽  
Local Entropy ◽  
Edge Preserving ◽  
Small Target Detection ◽  
Infrared Small Target

According to the larger false alarm rate resulted from fluctuant clutter, a novel method combined with bilateral filter and local entropy is proposed for infrared small target detection in this paper. Firstly, the original image is respectively processed by bilateral filter and local entropy, and then the two processed images are fused by point product to generate the background suppression map. Secondly, the guided filter is used to further suppress the background and enhance the small target in the map. Thirdly, the small target is detected by the adaptive threshold in the filtered map. The theoretical analyses and experimental results show that the proposed method not only effectively suppresses the clutter background, depending on the edge preserving and denoising characteristics of bilateral filtering, but also effectively highlights the small target, relying on the sensitivity of local entropy to the abrupt gray region. Compared with other methods, it is demonstrated that the proposed method owns lower false alarm rate and higher detection rate.

scholarly journals A Two-Dimensional Adaptive Target Detection Algorithm in the Compressive Domain

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 567 ◽  
Author(s):  
Wen-Huan Cao ◽  
Shu-Cai Huang
Keyword(s):  
Compressive Sensing ◽  
Target Detection ◽  
Adaptive Threshold ◽  
Computational Time ◽  
Gram Matrix ◽  
Small Target ◽  
Two Dimensional ◽  
Threshold Method ◽  
Small Target Detection ◽  
Infrared Small Target

By applying compressive sensing to infrared imaging systems, the sampling and transmitting time can be remarkably reduced. Therefore, in order to meet the real-time requirements of infrared small target detection tasks in the remote sensing field, many approaches based on compressive sensing have been proposed. However, these approaches need to reconstruct the image from the compressive domain before detecting targets, which is inefficient due to the complex recovery algorithms. To overcome this drawback, in this paper, we propose a two-dimensional adaptive threshold algorithm based on compressive sensing for infrared small target detection. Instead of processing the reconstructed image, our algorithm focuses on directly detecting the target in the compressive domain, which reduces both the time and memory requirements for image recovery. First, we directly subtract the spatial background image in the compressive domain of the original image sampled by the two-dimensional measurement model. Then, we use the properties of the Gram matrix to decode the subtracted image for further processing. Finally, we detect the targets by employing the advanced adaptive threshold method to the decoded image. Experiments show that our algorithm can achieve an average 100% detection rate, with a false alarm rate lower than 0.4%, and the computational time is within 0.3 s, on average.

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