基于深度学习的图像语义分割方法综述

Review of Image Semantic Segmentation Based on Deep Learning

近年来,深度学习技术已经广泛应用到图像语义分割领域.主要对基于深度学习的图像语义分割的经典方法与研究现状进行分类、梳理和总结.根据分割特点和处理粒度的不同,将基于深度学习的图像语义分割方法分为基于区域分类的图像语义分割方法和基于像素分类的图像语义分割方法.把基于像素分类的图像语义分割方法进一步细分为全监督学习图像语义分割方法和弱监督学习图像语义分割方法.对每类方法的代表性算法进行了分析介绍,并详细总结了每类方法的基本思想和优缺点,系统地阐述了深度学习对图像语义分割领域的贡献.对图像语义分割相关实验进行了分析对比,并介绍了图像语义分割实验中常用公共数据集和性能评价指标.最后,预测并分析总结了该领域未来可能的研究方向及相应的发展趋势.

Recent years, applying Deep Learning (DL) into Image Semantic Segmentation (ISS) has been widely used due to its state-of-the-art performances and high-quality results. This paper systematically reviews the contribution of DL to the field of ISS. Different methods of ISS based on DL (ISSbDL) are summarized. These methods are divided into ISS based on the Regional Classification (ISSbRC) and ISS based on the Pixel Classification (ISSbPC) according to the image segmentation characteristics and segmentation granularity. Then, the methods of ISSbPC are surveyed from two points of view: ISS based on Fully Supervised Learning (ISSbFSL) and ISS based on Weakly Supervised Learning (ISSbWSL). The representative algorithms of each method are introduced and analyzed, as well as the basic workflow, framework, advantages and disadvantages of these methods are detailedly analyzed and compared. In addition, the related experiments of ISS are analyzed and summarized, and the common data sets and performance evaluation indexes in ISS experiments are introduced. Finally, possible research directions and trends are given and analyzed.