Road sign detection and recognition using fuzzy artmap

a case study Swedish speed-limit signs

Document identifier: oai:dalea.du.se:2253
Keyword: Traffic signs, Color segmentation, Outdoor images, Fuzzy ARTMAP, Classification.
Publication year: 2006
Abstract:

In this paper, a novel approach is developed using Fuzzy ARTMAP Neural Networks to recognize and classify Swedish road and traffic signs. The Swedish Speed-Limit signs are selected as a case study, but the system can be applied to other signs. A new color detection and segmentation algorithm is presented in which the effects of shadows and highlights are eliminated. Images are taken by a digital camera mounted in a car. Segmented images are created by converting RGB images into HSV color space and applying the shadow-highlight invariant method. The method is tested on hundreds of outdoor images under shadow and highlight conditions, and it shows high robustness; in 95% of cases of correct segmentation is achieved. Classification is carried out by two stages of Fuzzy ARTMAP which are trained by 210 and 150 images, respectively. The first stage determines the border of the sign and the second stage determines the pictogram. Training and testing of both stages are made offline, using still images. In online mode, the system loads the Fuzzy ARTMAP and performs recognition process. An accuracy of 96.7% is achieved in Speed-Limit recognition and more than 90% as whole accuracy.

Authors

Hasan Fleyeh

Högskolan Dalarna; Datateknik
Other publications >>

Syed Gilani

Other publications >>

Mark Dougherty

Högskolan Dalarna; Datateknik
Other publications >>

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    header: 
        identifier: oai:dalea.du.se:2253
        datestamp: 2021-04-15T12:25:47Z
        setSpec: SwePub-du
    metadata: 
        mods: 
            @attributes: 
                version: 3.7
            recordInfo: 
                recordContentSource: du
                recordCreationDate: 2006-08-16
            identifier: http://urn.kb.se/resolve?urn=urn:nbn:se:du-2253
            titleInfo: 
                @attributes: 
                    lang: eng
                title: Road sign detection and recognition using fuzzy artmap
                subTitle: a case study Swedish speed-limit signs
            abstract: 
In this paper a novel approach is developed using Fuzzy ARTMAP Neural Networks to recognize and classify Swedish road and traffic signs. The Swedish Speed-Limit signs are selected as a case study but the system can be applied to other signs. A new color detection and segmentation algorithm is presented in which the effects of shadows and highlights are eliminated.  Images are taken by a digital camera mounted in a car. Segmented images are created by converting RGB images into HSV color space and applying the shadow-highlight invariant method. The method is tested on hundreds of outdoor images under shadow and highlight conditions and it shows high robustness; in 95% of cases of correct segmentation is achieved. \nClassification is carried out by two stages of Fuzzy ARTMAP which are trained by 210 and 150 images respectively.  The first stage determines the border of the sign and the second stage determines the pictogram. Training and testing of both stages are made offline using still images. In online mode the system loads the Fuzzy ARTMAP and performs recognition process. An accuracy of 96.7% is achieved in Speed-Limit recognition and more than 90% as whole accuracy.

            subject: 
                    @attributes: 
                        lang: eng
                    topic: Traffic signs
                    @attributes: 
                        lang: eng
                    topic: Color segmentation
                    @attributes: 
                        lang: eng
                    topic: Outdoor images
                    @attributes: 
                        lang: eng
                    topic: Fuzzy ARTMAP
                    @attributes: 
                        lang: eng
                    topic: Classification.
            language: 
                languageTerm: eng
            genre: 
                conference/other
                ref
            note: 
                Published
                3
            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
                    namePart: 
                        Gilani
                        Syed
                    role: 
                        roleTerm: aut
                    @attributes: 
                        type: personal
                        authority: du
                    namePart: 
                        Dougherty
                        Mark
                    role: 
                        roleTerm: aut
                    affiliation: 
                        Högskolan Dalarna
                        Datateknik
                    nameIdentifier: mdo
            originInfo: 
                dateIssued: 2006
                place: 
                    placeTerm: Palma de Mallorca Spain
            relatedItem: 
                @attributes: 
                    type: host
                titleInfo: 
                    title: The 10th IASTED International Conference on Artificial Intelligence and Soft Computing 
            physicalDescription: 
                form: print
            typeOfResource: text