摘要
鉴于深度学习在图像识别领域的重大进展,在无人直升机自主着舰的应用背景下,针对较为复杂的着舰环境和着舰标志设计,采用单级多区域检测(SSD)网络对着舰标志进行识别。针对SSD网络对小目标识别率低的缺点,基于深度残差网络和特征金字塔网络结构对SSD网络进行了改进,使用ResNet101代替VGG-16网络,并利用特征金字塔网络结构改进传统上采样结构,将检测网络的高层语义信息融入低层特征信息中,最后通过实验验证了改进网络的识别效果。
In view of the significant progress of deep learning in the field of image recognition,in the background of unmanned helicopter autonomous landing,SSD network is adopted to identify the landing mark for the complex landing environment and landing mark design.Aiming at the disadvantage of low recognition rate of small targets in SSD network,SSD network is improved based on deep residual network and feature pyramid network structure,and the ResNet101 is used instead of VGG-16 network,the traditional up-sampling structure is improved by using feature pyramid network structure,which integrates the high-level semantic information of the detection network into the low-level feature information.Finally,the recognition effect of the improved network is verified via experiments.
作者
吴鹏飞
石章松
黄隽
傅冰
WU Pengfei;SHI Zhangsong;HUANG Jun;FU Bing(College of Weaponry Engineering,Naval University of Engineering,Wuhan 430000,China)
出处
《电光与控制》
CSCD
北大核心
2022年第1期88-92,共5页
Electronics Optics & Control
基金
国家自然科学基金(61773395)。
关键词
着舰标志识别
SSD网络
图像识别
深度残差网络
特征金字塔网络结构
identification of landing mark
SSD network
image recognition
deep residual network
feature pyramid network structure