摘要
针对低空无人机目标视觉特征较弱,传统识别模型在目标尺度较小时易受干扰导致识别精度下降等问题,提出了一种基于多隐含层深度神经网络的弱小无人机目标检测模型。根据低空监视图像输入特性和弱小无人机目标视觉表征特点,设计了包含多个隐含层的多通道深度神经网络模型结构,并通过建立多尺度、多角度、多背景条件下的无人机目标图像数据库,完成了对深度网络模型参数的训练及优化。仿真结果表明,所设计的深度模型对低空无人机目标具有较好的变尺度检测能力和抗干扰效果,体现出良好的鲁棒性和潜在工程应用前景。
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.
作者
王靖宇
王霰禹
张科
蔡宜伦
刘越
Wang Jingyu;Wang Xianyu;Zhang Ke;Cai Yilun;Liu Yue(National Key Laboratory of Aerospace Flight Dynamics,Xi′an 710072,China;School of Astronautics,Northwestern Polytechnical University,Xi′an 710072,China)
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2018年第2期258-263,共6页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(61174204
61101191
61502391)
航天支撑基金(N2015KC0121)
航天飞行动力学技术重点实验室开放基金
陕西省自然科学基础研究计划(2017JM6043)
中央高校基本科研业务费资助
关键词
低空无人机
目标识别
深度神经网络
多隐含层
unmanned aerial vehicle(UAV)
object recognition
deep neural network(DNN)
multi-hidden layer
neural networks
optimization
