Constant false alarm rate algorithm for the dim-small target detection based on the distribution characteristics of target coordinates

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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SVM-Based Sea-Surface Small Target Detection: A False-Alarm-Rate-Controllable Approach

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2019.2894385 ◽

2019 ◽

Vol 16 (8) ◽

pp. 1225-1229 ◽

Cited By ~ 5

Author(s):

Yuzhou Li ◽

Pengcheng Xie ◽

Zeshen Tang ◽

Tao Jiang ◽

Peihan Qi

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Sea Surface ◽

Small Target ◽

Small Target Detection

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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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Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽

10.3390/s21051643 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1643

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Mengdao Xing ◽

Jingbiao Wei

Keyword(s):

Synthetic Aperture Radar ◽

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Synthetic Aperture ◽

False Alarms ◽

Sar Image ◽

Constant False Alarm Rate ◽

Sar Images ◽

Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

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Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

Remote Sensing ◽

10.3390/rs13091703 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1703

Author(s):

He Yan ◽

Chao Chen ◽

Guodong Jin ◽

Jindong Zhang ◽

Xudong Wang ◽

...

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Real Data ◽

Detection Performance ◽

Detection Accuracy ◽

Constant False Alarm Rate ◽

Data Set ◽

Detection Technology ◽

Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

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New matrix constant false alarm rate detectors for radar target detection

The Journal of Engineering ◽

10.1049/joe.2019.0454 ◽

2019 ◽

Vol 2019 (19) ◽

pp. 5597-5601 ◽

Cited By ~ 1

Author(s):

Wenjing Zhao ◽

Deyue Zou ◽

Wenlong Liu ◽

Minglu Jin

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Radar Target ◽

Constant False Alarm Rate

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Adjacent Infrared Multitarget Detection Using Robust Background Estimation

Journal of Sensors ◽

10.1155/2016/7279081 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Sungho Kim ◽

Kyung-Tae Kim

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Detection Method ◽

Detection Performance ◽

Detection Methods ◽

Constant False Alarm Rate ◽

Cluttered Environments ◽

Background Estimation ◽

Cfar Detection

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).

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