全卷积神经网络图像语义分割方法综述  

Image Semantic Segmentation Based on Fully Convolutional Neural Network

作　　者：张鑫[1] 姚庆安[1] 赵健 金镇君 冯云丛 ZHANG Xin;YAO Qing’an;ZHAO Jian;JIN Zhenjun;FENG Yuncong(School of Computer Science and Engineering,Changchun University of Technology,Changchun 130102,China)

机构地区：[1]长春工业大学计算机科学与工程学院,长春130102

出　　处：《计算机工程与应用》2022年第8期45-57,共13页Computer Engineering and Applications

基　　金：吉林省科技发展规划重点研发项目(20200401076GX);吉林省教育厅“十三五”科学技术研究规划项目(JJKH20200678KJ);符号计算与知识工程教育部重点实验室2020年度开放基金(93K172020K05)。

摘　　要：图像语义分割是计算机视觉领域的热点研究课题,随着全卷积神经网络的迅速兴起,图像语义分割和全卷积神经网络的融合发展取得了非常卓越的成绩。通过对近年来高质量文献的收集,重点对全卷积神经网络图像语义分割方法进行总结。将收集的文献,按照应用场景的不同,划分为经典语义分割、实时性语义分割和RGBD语义分割,对具有代表性的分割方法进行阐述。同时归纳了常用的公共数据集和性能的评价指标,并对常用数据集上的实验进行分析总结,对全卷积神经网络未来可能的研究方向进行展望。Image semantic segmentation is a hot research topic in the field of computer vision. With the rapid rise of fully convolutional neural networks, the development of fusion of image semantic segmentation and fully convolutional networks has shown very bright results. Through the collection of high-quality literature in recent years, the focus is on the summary of full convolutional neural network image semantic segmentation methods. The collected literature is divided into classical semantic segmentation, real-time semantic segmentation and RGBD semantic segmentation according to the application scenarios, and then the representative segmentation methods are described. Commonly used public datasets and evaluation metrics for performance are also summarized, and experiments on commonly used datasets are analyzed and summarized. Finally, the possible future research directions of fully convolutional neural networks are prospected.

关 键 词：图像语义分割 计算机视觉 全卷积神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]