摘要
自FCN网络在2014年提出后,SegNet、DeepLab等一系列关于图像语义分割的深度学习架构被相继提出。与传统方法相比,这些架构效果更好、运算速度更快,已经能够运用于自然图像的分割处理。围绕图像语义分割技术,对常用的数据集和典型网络架构进行了梳理分析,对2017年以来的新进展进行了综合研究,利用主流评价指标对主要模型的语义分割效果进行了比较和分析。对语义分割技术面临的挑战以及可能的发展趋势进行了展望。
Since the FCN network was proposed in 2014,a series of deep learning architectures for image semantic segmentation such as SegNet and DeepLab have been proposed.Compared with the traditional methods,these architectures are better and faster,and can be applied to the segmentation of natural images.This paper focuses on the image semantic segmentation technology.The commonly used data sets and typical network architectures are analyzed.And a comprehensive study is conducted about the new progress since 2017.The current evaluation indicators are used to compare and analyze the semantic segmentation effects of the main models.Finally,the challenges faced by semantic segmentation technology and the possible development trends are prospected.
作者
梁新宇
罗晨
权冀川
肖铠鸿
高伟嘉
LIANG Xinyu;LUO Chen;QUAN Jichuan;XIAO Kaihong;GAO Weijia(College of Command&Control Engineering,Army Engineering University of PLA,Nanjing 210007,China;College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China;Unit 68023 of PLA,China)
出处
《计算机工程与应用》
CSCD
北大核心
2020年第2期18-28,共11页
Computer Engineering and Applications
关键词
深度学习
图像分割
语义分割
deep learning
image segmentation
semantic segmentation