Research on the Progress of Image Instance Segmentation Based on Deep Learning

LIANG Xin-yu; LIN Xi-kun; QUAN Ji-chuan; XIAO Kai-hong

doi:10.3969/j.issn.0372-2112.2020.12.025

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Research on the Progress of Image Instance Segmentation Based on Deep Learning

更新时间：2025-12-08

- Research on the Progress of Image Instance Segmentation Based on Deep Learning
- Acta Electronica Sinica Vol. 48, Issue 12, Pages: 2476-2486(2020)
- 作者机构：
  
  1. 陆军工程大学指挥控制工程学院,江苏,南京,210007
  2. 中国人民解放军 73676部队,江苏,无锡,214400
  4. 华中科技大学软件学院,湖北,武汉,430070
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.12.025
  CLC： TP183
- Published Online：25 December 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
LIANG Xin-yu, LIN Xi-kun, QUAN Ji-chuan, et al. Research on the Progress of Image Instance Segmentation Based on Deep Learning[J]. Acta Electronica Sinica, 2020, 48(12): 2476-2486.
DOI：

LIANG Xin-yu, LIN Xi-kun, QUAN Ji-chuan, et al. Research on the Progress of Image Instance Segmentation Based on Deep Learning[J]. Acta Electronica Sinica, 2020, 48(12): 2476-2486. DOI： 10.3969/j.issn.0372-2112.2020.12.025.

摘要

随着深度学习算法在图像分割领域的成功应用，在图像实例分割方向上涌现出一大批优秀的算法架构.这些架构在分割效果、运行速度等方面都超越了传统方法.本文围绕图像实例分割技术的最新研究进展，对现阶段经典网络架构和前沿网络架构进行梳理总结，结合常用数据集和权威评价指标对各个架构的分割效果进行比较和分析.最后，对目前图像实例分割技术面临的挑战以及可能的发展趋势进行了展望.

Abstract

With the successful application of deep learning algorithms in the field of image segmentation

a large number of excellent algorithm architectures have emerged in the direction of image instance segmentation. These architectures surpass the traditional methods in terms of segmentation effects and running speed. This paper focuses on the latest research progress of image instance segmentation technology

summarizes the current classic network architecture and cutting-edge network architecture

and uses common datasets and authoritative evaluation indicators to compare and analyze the segmentation effects of each architecture. Finally

the challenges and possible development trends of image instance segmentation technology are prospected.

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