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Infrared small target detection algorithm based on potential regions proposal
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Paper Abstract

A novel infrared small target detection algorithm based on potential regions proposal is proposed in this paper. Potential regions mean subsets (size are 16 by 16 in this paper) with small targets of an infrared image. A convolution neural network (CNN) classifier has been trained by using constructed datasets to discriminate potential regions of an input image. Traditional methods such as tophat transform, max-mean and max-median filter are used to suppress the background and noise of potential regions. Some experiments are carried out to verify the algorithm performance, and the results show that the gains of signal noise ratio and contrast ratio have better performance than traditional methods.

Paper Details

Date Published: 12 March 2019
PDF: 9 pages
Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 110234R (12 March 2019); doi: 10.1117/12.2516772
Show Author Affiliations
Shuaihao Wang, Shanghai Institute of Mechanical and Electrical Engineering (China)
Jiangpeng Du, Shanghai Institute of Mechanical and Electrical Engineering (China)
Juanfang Chai, Shanghai Institute of Mechanical and Electrical Engineering (China)
Yiji Liu, Shanghai Institute of Mechanical and Electrical Engineering (China)
Chengshi Tang, Shanghai Institute of Mechanical and Electrical Engineering (China)


Published in SPIE Proceedings Vol. 11023:
Fifth Symposium on Novel Optoelectronic Detection Technology and Application
Qifeng Yu; Wei Huang; You He, Editor(s)

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