计算机科学

计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 10-24.doi: 10.11896/jsjkx.210200038

• 计算机图形学&多媒体* • 上一篇    下一篇

深度卷积神经网络图像实例分割方法研究进展

胡伏原1,2, 万新军1,3, 沈鸣飞1, 徐江浪1,3, 姚睿4, 陶重犇1,2   

  1. 1 苏州科技大学电子与信息工程学院 江苏 苏州215009
    2 苏州科技大学苏州市虚拟现实智能交互及应用技术重点实验室 江苏 苏州215009
    3 苏州科技大学苏州市大数据与信息服务重点实验室 江苏 苏州 215009
    4 中国矿业大学计算机科学与技术学院 江苏 徐州221116
  • 收稿日期:2021-02-03 修回日期:2021-07-09 出版日期:2022-05-15 发布日期:2022-05-06
  • 通讯作者: 万新军(wanxinjun1030@126.com)
  • 作者简介:(fuyuanhu@usts.edu.cn)
  • 基金资助:
    国家自然科学基金(61876121,61801323);江苏省重点研发计划项目(BE2017663);江苏省高等教育自然科学研发项目(19KJB520054,19KJB110021,20KJB520018)

Survey Progress on Image Instance Segmentation Methods of Deep Convolutional Neural Network

HU Fu-yuan1,2, WAN Xin-jun1,3, SHEN Ming-fei1, XU Jiang-lang1,3, YAO Rui4, TAO Zhong-ben1,2   

  1. 1 School of Electronic & Information Engineering,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China
    2 Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China
    3 Suzhou Key Laboratory for Big Data and Information Service,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China
    4 School of Computer Science and Technology,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China
  • Received:2021-02-03 Revised:2021-07-09 Online:2022-05-15 Published:2022-05-06
  • About author:HU Fu-yuan,born in 1978,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include machine learning and computer vision.
    WAN Xin-jun,born in 1996,postgra-duate,is a member of China Computer Federation.His main research interests include deep learning and computer vision.
  • Supported by:
    National Natural Science Foundation of China(61876121,61801323),Primary Research & Development Plan of Jiangsu Province(BE2017663) and Foundation of Natural Science Research Program in Jiangsu Province Higher Education(19KJB520054,19KJB110021,20KJB520018).

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摘要： 图像实例分割是图像处理和计算机视觉技术中关于图像理解的重要环节,随着深度学习和深层卷积神经网络日趋成熟,基于深度卷积神经网络的图像实例分割方法取得了跨越性进展。实例分割任务实际上是目标检测和语义分割两项任务的结合,可以在像素层面完成识别图像中目标轮廓的任务。实例分割不仅可以定位图像中目标的位置,从像素层面上分割所有目标,还可以标注出图像中同一类别的不同个体,既是对图像的像素级分割,又是实例级理解。首先,阐述了图像实例分割产生的原因和深度卷积神经网络的作用。然后,根据图像实例分割方法的过程和特征,分别从两阶段和单阶段的角度介绍了图像实例分割的研究进展,详细阐述了两类方法的优势和不足,进而总结了各类实例分割方法对区域、特征提取和掩膜的设计思路。此外,归纳了图像实例分割方法的性能评价标准和常用的公开数据集,并在此基础上对比和评估了主流的图像实例分割模型的分割精度。最后,指出了当前图像实例分割存在的问题及解决思路,并对其未来发展进行了总结和展望。

关键词: 单阶段, 两阶段, 目标检测, 深度卷积神经网络, 实例分割, 语义分割

Abstract: Image instance segmentation is an important part of image processing and computer vision technology about image understanding.With the development of deep learning and deep convolutional neural network,image instance segmentation method based on deep convolutional neural network has made great progress.Instance segmentation task is actually the combination of target detection and semantic segmentation,which can complete the task of recognizing the target contour in the image at thepixel level.Instance segmentation can not only locate the position of the object in the image,segment all the objects from the pixel level,but also mark different individuals of the same category in the image,which is not only the pixel level segmentation of the image,but also the instance level understanding.Firstly,the reason of image segmentation and the function of deep convolution neural network are described.Then,according to the process and characteristics of image instance segmentation methods,the research progress of image instance segmentation is introduced from two-stage and single-stage perspectives,and the advantages and disadvantages of the two methods are described in detail.Then,the design ideas of region,feature extraction and mask are summarized.In addition,the performance evaluation criteria and common public data sets of image instance segmentation methods are summarized,and on this basis,the segmentation accuracy of mainstream image instance segmentation models is compared and evaluated.Finally,it points out the problems and solutions of the current image instance segmentation,summarizes the development of image instance segmentation and prospects for the future.

Key words: Deep convolutional neural network, Instance segmentation, Object detection, Semantic segmentation, Single stage, Two stage

中图分类号: 

  • TP391.4
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