摘要
针对目前武器装备在检测空中远距离弱小目标、假目标、遮挡等情况中智能化程度不高问题,分析了卷积神经网络的工作方式以及其应用在目标检测中的优势,讨论了基于卷积神经网络的目标检测算法在其它图像检测领域的应用情况及取得的最新成果,通过研究发现卷积神经网络利用其强大的特征学习能力使得检测过程更为高效化、智能化,将其应用到导弹武器系统中是未来提升防空作战效能的必然手段。
In view of the fact that the current weaponry is not intelligent in detecting long-range and weak targets,false targets,and occlusions in the air. The working mode of convolutional neural network and the advantages of its application in target detection are analyzed. The application and the latest achievements of the target detection algorithm based on convolutional neural network in other image detection fields are discussed. Convolutional neural networks use their powerful feature learning capabilities to make the detection process more efficient and intelligent is discovered through research. Its application to the missile weapon system is the inevitable means to improve the effectiveness of air defense operations in the future.
作者
姜晓伟
王春平
付强
Jiang Xiaowei;Wang Chunping;Fu Qiang(Electronic and Optical Department,Shijiazhuang Campus,Army Engineering University of PLA,Shijiazhuang 050000,China)
出处
《战术导弹技术》
北大核心
2019年第1期108-114,123,共8页
Tactical Missile Technology
关键词
卷积神经网络
检测算法
武器系统
目标检测
convolutional neural network
detection algorithm
weapon system
target detection
