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Proceedings Paper

Improved detection probability of low level light and infrared image fusion system
Author(s): Yuxiang Luo; Rongguo Fu; Junju Zhang; Wencong Wang; Benkang Chang
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Paper Abstract

Low level light(LLL) image contains rich information on environment details, but is easily affected by the weather. In the case of smoke, rain, cloud or fog, much target information will lose. Infrared image, which is from the radiation produced by the object itself, can be "active" to obtain the target information in the scene. However, the image contrast and resolution is bad, the ability of the acquisition of target details is very poor, and the imaging mode does not conform to the human visual habit. The fusion of LLL and infrared image can make up for the deficiency of each sensor and give play to the advantages of single sensor. At first, we show the hardware design of fusion circuit. Then, through the recognition probability calculation of the target(one person) and the background image(trees), we find that the trees detection probability of LLL image is higher than that of the infrared image, and the person detection probability of the infrared image is obviously higher than that of LLL image. The detection probability of fusion image for one person and trees is higher than that of single detector. Therefore, image fusion can significantly enlarge recognition probability and improve detection efficiency.

Paper Details

Date Published: 20 February 2018
PDF: 5 pages
Proc. SPIE 10697, Fourth Seminar on Novel Optoelectronic Detection Technology and Application, 106972N (20 February 2018); doi: 10.1117/12.2317475
Show Author Affiliations
Yuxiang Luo, Shandong Institute of Aerospace Electronics Technology (China)
Rongguo Fu, Nanjing Univ. of Science and Technology (China)
Junju Zhang, Nanjing Univ. of Science and Technology (China)
Wencong Wang, Shandong Institute of Aerospace Electronics Technology (China)
Benkang Chang, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10697:
Fourth Seminar on Novel Optoelectronic Detection Technology and Application
Weiqi Jin; Ye Li, Editor(s)

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