Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.

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INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON FEATURE SALIENCE AND MULTI-FEATURES FUSION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001411008579 ◽

2011 ◽

Vol 25 (02) ◽

pp. 299-308 ◽

Cited By ~ 6

Author(s):

ZHEN-XUE CHEN ◽

CHENG-YUN LIU ◽

FA-LIANG CHANG

Keyword(s):

Target Detection ◽

Detection Algorithm ◽

Probability Of Detection ◽

Absolute Difference ◽

Small Target ◽

Probability Of Error ◽

Small Target Detection ◽

Low Snr ◽

Complex Background ◽

Small Targets

It is an important and challenging problem to detect small targets in clutter scene and low SNR (Signal Noise Ratio) in infrared (IR) images. In order to solve this problem, a method based on feature salience is proposed for automatic detection of targets in complex background. Firstly, in this paper, the method utilizes the average absolute difference maximum (AADM) as the dissimilarity measurement between targets and background region to enhance targets. Secondly, minimum probability of error was used to build the model of feature salience. Finally, by computing the realistic degree of features, this method solves the problem of multi-feather fusion. Experimental results show that the algorithm proposed shows better performance with respect to the probability of detection. It is an effective and valuable small target detection algorithm under a complex background.

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Infrared Small Target Detection via Modified Random Walks

Remote Sensing ◽

10.3390/rs10122004 ◽

2018 ◽

Vol 10 (12) ◽

pp. 2004 ◽

Cited By ~ 12

Author(s):

Chaoqun Xia ◽

Xiaorun Li ◽

Liaoying Zhao

Keyword(s):

Random Walks ◽

Target Detection ◽

Detection Algorithm ◽

Local Contrast ◽

Small Target ◽

Global Uniqueness ◽

Dimensional Image ◽

Small Target Detection ◽

Small Targets ◽

Infrared Small Target

Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of remote sensing. Conventional algorithms can fail in detecting small targets due to the low signal-to-noise ratios of the images. To solve this problem, an effective infrared small target detection algorithm inspired by random walks is presented in this paper. The novelty of our contribution involves the combination of the local contrast feature and the global uniqueness of the small targets. Firstly, the original pixel-wise image is transformed into an multi-dimensional image with respect to the local contrast measure. Secondly, a reconstructed seeds selection map (SSM) is generated based on the multi-dimensional image. Then, an adaptive seeds selection method is proposed to automatically select the foreground seeds potentially placed in the areas of the small targets in the SSM. After that, a confidence map is constructed using a modified random walks (MRW) algorithm to represent the global uniqueness of the small targets. Finally, we segment the targets from the confidence map by utilizing an adaptive threshold. Extensive experimental evaluation results on a real test dataset demonstrate that our algorithm is superior to the state-of-the-art algorithms in both target enhancement and detection performance.

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Infrared Small Target Detection under Complex Background

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.346.615 ◽

2011 ◽

Vol 346 ◽

pp. 615-619 ◽

Cited By ~ 1

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.

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