Road and traffic sign detection and recognition
Document identifier: oai:dalea.du.se:1426
Keyword: Traffic signs,
Sign detection,
Sign recognitionPublication year: 2005Relevant Sustainable Development Goals (SDGs):
The SDG label(s) above have been assigned by OSDG.aiAbstract: This paper presents an overview of the road and traffic sign detection and recognition. It describes the characteristics of the road signs, the requirements and difficulties behind road signs detection and recognition, how to deal with outdoor images, and the different techniques used in the image segmentation based on the colour analysis, shape analysis. It shows also the techniques used for the recognition and classification of the road signs. Although image processing plays a central role in the road signs recognition, especially in colour analysis, but the paper points to many problems regarding the stability of the received information of colours, variations of these colours with respect to the daylight conditions, and absence of a colour model that can led to a good solution. This means that there is a lot of work to be done in the field, and a lot of improvement can be achieved. Neural networks were widely used in the detection and the recognition of the road signs. The majority of the authors used neural networks as a recognizer, and as classifier. Some other techniques such as template matching or classical classifiers were also used. New techniques should be involved to increase the robustness, and to get faster systems for real-time applications.
Authors
Hasan Fleyeh
Högskolan Dalarna; Datateknik
Other publications
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Mark Dougherty
Högskolan Dalarna; Datateknik
Other publications
>>
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header:
identifier: oai:dalea.du.se:1426
datestamp: 2021-04-15T12:05:57Z
setSpec: SwePub-du
metadata:
mods:
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version: 3.7
recordInfo:
recordContentSource: du
recordCreationDate: 2005-08-27
identifier: http://urn.kb.se/resolve?urn=urn:nbn:se:du-1426
titleInfo:
@attributes:
lang: eng
title: Road and traffic sign detection and recognition
abstract: This paper presents an overview of the road and traffic sign detection and recognition. It describes the characteristics of the road signs the requirements and difficulties behind road signs detection and recognition how to deal with outdoor images and the different techniques used in the image segmentation based on the colour analysis shape analysis. It shows also the techniques used for the recognition and classification of the road signs. Although image processing plays a central role in the road signs recognition especially in colour analysis but the paper points to many problems regarding the stability of the received information of colours variations of these colours with respect to the daylight conditions and absence of a colour model that can led to a good solution. This means that there is a lot of work to be done in the field and a lot of improvement can be achieved. Neural networks were widely used in the detection and the recognition of the road signs. The majority of the authors used neural networks as a recognizer and as classifier. Some other techniques such as template matching or classical classifiers were also used. New techniques should be involved to increase the robustness and to get faster systems for real-time applications.
subject:
@attributes:
lang: eng
topic: Traffic signs
@attributes:
lang: eng
topic: sign detection
@attributes:
lang: eng
topic: sign recognition
language:
languageTerm: eng
genre:
conference/other
ref
note:
Published
2
name:
@attributes:
type: personal
authority: du
namePart:
Fleyeh
Hasan
role:
roleTerm: aut
affiliation:
Högskolan Dalarna
Datateknik
nameIdentifier:
hfl
0000-0002-1429-2345
@attributes:
type: personal
authority: du
namePart:
Dougherty
Mark
role:
roleTerm: aut
affiliation:
Högskolan Dalarna
Datateknik
nameIdentifier: mdo
originInfo:
dateIssued: 2005
place:
placeTerm: Poznan Poland
relatedItem:
@attributes:
type: host
titleInfo:
title: 10th EWGT Meeting and 16th Mini-EURO Conference
physicalDescription:
form: print
typeOfResource: text