A New U-Net Based License Plate Enhancement Model in Night and Day Images

Chowdhury, PN; Shivakumara, P; Raghavendra, R; Pal, U; Lu, T; Blumenstein, M

A New U-Net Based License Plate Enhancement Model in Night and Day Images

Chowdhury, PN Shivakumara, P Raghavendra, R Pal, U Lu, T Blumenstein, M

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Pattern Recognition, 2020, 12046 LNCS, pp. 749-763
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Chowdhury, PN
dc.contributor.author	Shivakumara, P
dc.contributor.author	Raghavendra, R
dc.contributor.author	Pal, U
dc.contributor.author	Lu, T
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date	2019-11-26
dc.date.accessioned	2021-04-09T06:56:44Z
dc.date.available	2021-04-09T06:56:44Z
dc.date.issued	2020-01-01
dc.identifier.citation	Pattern Recognition, 2020, 12046 LNCS, pp. 749-763
dc.identifier.isbn	9783030414030
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/147963
dc.description.abstract	A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Pattern Recognition
dc.relation.ispartof	ACPR
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-41404-7_53
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A New U-Net Based License Plate Enhancement Model in Night and Day Images
dc.type	Conference Proceeding
utslib.citation.volume	12046 LNCS
utslib.location.activity	Auckland, New Zealand
utslib.for	0801 Artificial Intelligence and Image Processing
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
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-09T06:56:43Z
pubs.finish-date	2019-11-29
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2019-11-26
pubs.volume	12046 LNCS
dc.location	Switzerland

Abstract:

A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection.

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http://hdl.handle.net/10453/147963