[1]SU Li,SUN Yuxin,YUAN Shouzheng.A survey of instance segmentation research based on deep learning[J].CAAI Transactions on Intelligent Systems,2022,17(1):16-31.[doi:10.11992/tis.202109043]
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A survey of instance segmentation research based on deep learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 16-31 Column: 综述 Public date: 2022-01-05
Title:
A survey of instance segmentation research based on deep learning
Author(s):
SU Li1; 2;  SUN Yuxin1;  YUAN Shouzheng1
1. College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China;
2. Key Laboratory of Ministry of Education on Intelligent Technology and Application of Marine Equipment, Harbin Engineering University, Harbin 150001, China
Keywords:
computer vision; instance segmentation; image segmentation; convolutional neural network; deep learning; object detection; two-stage instance segmentation; one-stage instance segmentation
CLC:
TP183
DOI:
10.11992/tis.202109043
Abstract:
Deep learning has made great progress in the field of computer vision. Although instance segmentation research based on deep learning has only become a research hotpot in recent years, relevant techniques can be widely used in the fields of autonomous driving, complementary medicine and remote sensing imaging. Instance segmentation, as one of the fundamental problems of computer vision, requires not only pixel-level segmentation of different classes of targets, but also differentiation of different targets. In addition, the flexibility of target shapes, the occlusion between different targets and the tedious data annotation problems all make the instance segmentation task extremely challenging. In this paper, firstly, some valuable research results in instance segmentation are systematically reviewed according to two-stage instance segmentation and one-stage instance segmentation. Secondly, the advantages and disadvantages of different algorithms are analyzed and the testing performance of different models on the COCO dataset is compared. In addition, the applications of instance segmentation under special conditions are summarized, and common datasets and evaluation metrics are briefly introduced. Finally, the possible future directions of instance segmentation and the challenges it faces are prospected.
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