Infrared small target detection based on region proposal and CNN classifier

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

Sensors ◽

10.3390/s20030755 ◽

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.

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Infrared Small Target Detection Method Combined with Bilateral Filter and Local Entropy

Security and Communication Networks ◽

10.1155/2021/6661852 ◽

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.

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