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 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.

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 based on modified local entropy and EMD

2010 ◽  
Vol 8 (1) ◽  
pp. 24-28 ◽  
Author(s):  
邓鹤 He Deng ◽  
刘建国 Jianguo Liu ◽  
陈忠 Zhong Chen
Keyword(s):  
Target Detection ◽  
Small Target ◽  
Local Entropy ◽  
Small Target Detection ◽  
Infrared Small Target

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.

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