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Trajectory and image-based detection and identification of UAV

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Abstract

Much more attentions have been attracted to the inspection and prevention of unmanned aerial vehicle (UAV) in the wake of increasing high frequency of security accident. Many factors like the interferences and the small fuselage of UAV pose challenges to the timely detection of the UAV. In our work, we present a system that is capable of detecting, recognizing, and tracking an UAV using single camera automatically. For our method, a single pan–tilt–zoom (PTZ) camera detects flying objects and gets their trajectories; then, the trajectory identified as a UAV guides the camera and PTZ to capture the detailed region image of the target. Therefore, the images can be classified into the UAV and interference classes (such as birds) by the convolution neural network classifier trained with our image dataset. For the target recognized as a UAV with the double verification, the radio jammer emits the interferential radio to disturb its control radio and GPS. This system could be applied in some complex environment where many birds and UAV appear simultaneously.

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Acknowledgements

This research was partially supported by research grants from the National Natural Science Foundation of China (Grant No.: 61571314) and the Sky Defence Technology Co., Ltd.

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Authors and Affiliations

  1. College of Electrical Engineering, Sichuan University, Chengdu, 610065, China

    Yicheng Liu, Luchuan Liao, Ling He, Gang Yang, Han Zhang & Jing Zhang

  2. Sky Defence Technology, Chengdu, 610213, China

    Hao Wu

  3. Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, Hung Hom, China

    Jing Qin

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  1. Yicheng Liu
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  2. Luchuan Liao
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  7. Han Zhang
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Correspondence to Jing Zhang.

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Liu, Y., Liao, L., Wu, H. et al. Trajectory and image-based detection and identification of UAV. Vis Comput 37, 1769–1780 (2021). https://doi.org/10.1007/s00371-020-01937-y

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