基于深度学习的图像分割综述  

Image Segmentation Based on Deep Learning:A Survey

作　　者：黄雯珂 滕飞[1] 王子丹 冯力 HUANG Wenke;TENG Fei;WANG Zidan;FENG Li(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学计算机与人工智能学院,成都611756

出　　处：《计算机科学》2024年第2期107-116,共10页Computer Science

摘　　要：图像分割是计算机视觉中的一项基本任务,其主要目的是从图像输入中提取有意义和连贯的区域。多年来,图像分割领域已经开发出了各种各样的技术,包括基于传统方法,以及利用卷积神经网络的最新图像分割技术。随着深度学习的发展,更多的深度学习算法也被应用到图像分割任务中。特别地,近两年学者对深度学习的兴趣高涨,涌现了许多应用于图像分割任务的深度学习算法。然而大部分新的算法还没有被归纳分析,这将不利于后续研究的进行。文中对近两年发表的基于深度学习的图像分割研究进行了全面回顾。首先对图像分割的常用数据集进行简要介绍,然后阐明了基于深度学习的图像分割的新分类,最后讨论了现有的挑战并对今后的研究方向进行了展望。Image segmentation is a fundamental task in computer vision and its main purpose is to extract meaningful and cohe-rent regions from the image input.Over the years,a wide variety oftechniques have been developed in the field of image segmentation,including those based on traditional methods,as well as more recent image segmentation techniques utilizing convolutional neural networks.With the development of deep learning,more deep learning algorithms have been applied to image segmentation tasks.In particular,there has been a surge of scholarly interest in deep learning over the past two years,and many deep learning algorithms have emerged for image segmentation tasks.However,most of the new algorithms have not been summarized or analyzed,which will hinder the progress of subsequent research.This paper provides a comprehensive review of literatures on deep learning-based image segmentation research published in the past two years.First,it briefly introduces common datasets for image segmentation.Next,it clarifies new classifications for image segmentation based on deep learning.Finally,the existing challenges are discussed and the future research directions are prospected.

关 键 词：图像分割 语义分割 深度学习 网络结构 监督学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]