A Survey on Object Instance Segmentation

Sharma, R; Saqib, M; Lin, CT; Blumenstein, M

A Survey on Object Instance Segmentation

Sharma, R Saqib, M Lin, CT Blumenstein, M

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: SN Computer Science, 2022, 3, (6), pp. 499
Issue Date:: 2022-11-01

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Field	Value	Language
dc.contributor.author	Sharma, R
dc.contributor.author	Saqib, M
dc.contributor.author	Lin, CT
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2023-03-19T23:31:26Z
dc.date.available	2023-03-19T23:31:26Z
dc.date.issued	2022-11-01
dc.identifier.citation	SN Computer Science, 2022, 3, (6), pp. 499
dc.identifier.issn	2662-995X
dc.identifier.issn	2661-8907
dc.identifier.uri	http://hdl.handle.net/10453/167620
dc.description.abstract	In recent years, instance segmentation has become a key research area in computer vision. This technology has been applied in varied applications such as robotics, healthcare and intelligent driving. Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation concurrently. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. Further, we will discuss about its development in this field along with the most common datasets used. We will also focus on different challenges and future development scope for instance segmentation. This technology will provide a strong reference for future researchers in our survey paper.
dc.language	en
dc.publisher	Springer Nature
dc.relation	http://purl.org/au-research/grants/nhmrc/APP2001653
dc.relation	Defence Science and Technology Group of the Department of Defence
dc.relation	Positive Prime Technology P/L
dc.relation.ispartof	SN Computer Science
dc.relation.isbasedon	10.1007/s42979-022-01407-3
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Survey on Object Instance Segmentation
dc.type	Journal Article
utslib.citation.volume	3
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-19T23:31:25Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	3
utslib.citation.issue	6

Abstract:

In recent years, instance segmentation has become a key research area in computer vision. This technology has been applied in varied applications such as robotics, healthcare and intelligent driving. Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation concurrently. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. Further, we will discuss about its development in this field along with the most common datasets used. We will also focus on different challenges and future development scope for instance segmentation. This technology will provide a strong reference for future researchers in our survey paper.

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http://hdl.handle.net/10453/167620