Volume 36, Number 2, April 2018
|Page(s)||258 - 263|
|Published online||03 July 2018|
Small UAV Target Detection Model Based on Deep Neural Network
National Key Laboratory of Aerospace Flight Dynamics, Xi'an 710072, China
2 School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China
Unmanned aerial vehicle (UAV) has relatively small size and weak visual characteristics. The recognition accuracy of traditional object detection methods can decrease sharply when complex background and distraction objects exist. In this paper, we proposed a novel deep neural network (DNN) model for small UAV target recognition task. Based on the visual characteristics of surveillance image and UAV target, a multi-channel DNN is designed. Training and optimization of the DNN are completed with self-constructed UAV image database. Simulation results show that the proposed DNN model can achieve good results in recognizing the variable-scale UAV target and have compatible performance in distinguishing the interference and that the proposed model is robust and has a great potential prospect for engineering application.
Key words: unmanned aerial vehicle(UAV) / object recognition / deep neural network(DNN) / multi-hidden layer / neural networks / optimization
关键字 : 低空无人机 / 目标识别 / 深度神经网络 / 多隐含层
© 2018 Journal of Northwestern Polytechnical University. All rights reserved.
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