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Journal of Vcibration Testing and System Dynamics 4(2) (2020) 183--190 | DOI:10.5890/JVTSD.2020.06.005

Lin-Lu Dong$^{1}$, Zhi-Shuang Xue$^{1}$, Liang-Jun Zhao$^{2}$, Xiao-Shi Shi$^{1}$

$^{1}$ School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China

$^{2}$ Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Zigong 643000, China

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Abstract

The method of aircraft positioning in the anti-UAV(unmanned aerial vehicle, UAV) system is detecting objects through an image. Its preprocessing process needs to extract the edge of the target aircraft, however, due to the influence of image acquisition technology and electromagnetic environment, the obtained aircraft image is often interfered by a lot of noise. The traditional edge detection algorithm and the method of using local uniform sparsity to enhance the features of the aircraft can not meet the engineering requirement of detecting the edge of the aircraft which has complex structure. Aiming at this problem, an algorithm of edge extraction for aircraft under strong noise is proposed. Firstly, searching high definition images of other aircrafts similar to the structure of the target aircraft in the database, the algorithm is used to learn the texture features of these images. Then, the missing texture features corrupted by strong noise are repaired, this process can achieve the goals of enhancing the target aircraft's detail features and weakening influence of the background's features. Finally, the edge of the target aircraft is successfully detected. The experimental results show that the proposed algorithm can effectively detect the edge of the aircraft under strong noise.

Acknowledgments

This research was supported in part by the Project of Sichuan Department of Science and Technology under Grant 2017 GZ0303, in part by the Project of Monitoring Technology of Anti-terrorism Stability in Xinjiang based on High Resolution data under Grant 95-Y20A10-9001-16/17 and in part by the Opening Project of Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things under Grant 2019WZY04.

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