Detekcija i raspoznavanje prometnih znakova umjetnim neuronskim mrežama

Fištrek, Tomislav

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scientific master's theses

Detekcija i raspoznavanje prometnih znakova umjetnim neuronskim mrežama

Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2011. urn:nbn:hr:168:877810

Fištrek, Tomislav

University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electronic Systems and Information Processing

Cite this document

APA 6th Edition

Fištrek, T. (2011). Detekcija i raspoznavanje prometnih znakova umjetnim neuronskim mrežama (Scientific master's theses). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:877810

MLA 8th Edition

Fištrek, Tomislav. "Detekcija i raspoznavanje prometnih znakova umjetnim neuronskim mrežama." Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, 2011. https://urn.nsk.hr/urn:nbn:hr:168:877810

Chicago 17th Edition

Harvard

Fištrek, T. (2011). 'Detekcija i raspoznavanje prometnih znakova umjetnim neuronskim mrežama', Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:877810

Vancouver

Fištrek T. Detekcija i raspoznavanje prometnih znakova umjetnim neuronskim mrežama [Scientific master's theses]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2011 [cited 2024 April 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:877810

IEEE

T. Fištrek, "Detekcija i raspoznavanje prometnih znakova umjetnim neuronskim mrežama", Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2011. Available at: https://urn.nsk.hr/urn:nbn:hr:168:877810

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:877810

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Metadata

Title	Detekcija i raspoznavanje prometnih znakova umjetnim neuronskim mrežama
Title (english)	Traffic sign detection and recognition using neural networks
Author	Tomislav Fištrek
Mentor	Sven Lončarić (mentor)
Committee member	Sven Lončarić (predsjednik povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing (Department of Electronic Systems and Information Processing) Zagreb
Defense date and country	2011, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Electrical Engineering
Universal decimal classification (UDC)	621.3 - Electrical engineering
Abstract	Rješenje raspoznavanja prometnih znakova ograničavanja brzine realizirano je u dva osnovna dijela. Prvi dio je detekcija ruba znaka koja uključuje segmentaciju velikim skupom različitih pragova u različitim prostorima boja. Za svaku ulaznu sliku pogodan prag i komponenta iz nekog od prostora boja odabire se promatranjem histograma cijele slike. Posebno istrenirana umjetna neuronska mreža odlučuje o odabiru. Na taj način dobijamo sustav koji je prilagodljiv različitim uvjetima osvjetljenja te se segmentacija uspješno provodi i u uvjetima noćne rasvjete gdje prevladavaju crveni tonovi. Nagon segmentacije u prvom dijelu provodi se detekcija Houhovom transformacijom za kružnice u većem opsegu raspona radijusa i zbog toga je sustav prilagodljiv različitim veličinama prometnog znaka. Drugi dio je raspoznavanje gdje je najvažniji dio odabir pravih uzoraka i značajki. Prvi dio, tj. modul za detekciju daje jednu ili dvije najbolje ocjenjene kružnice u segmentiranoj slici smanjene rezolucije. Sada se u originalnoj slici nalazi odgovarajući okvir i on se smanjuje na određenu dimenziju te prebacuje u sivu sliku. Prema tako dobivenoj unutrašnjosti prometnog znaka sustav automatski priprema ulaze za NN koja raspoznaje znakove.
Abstract (english)	Speed limit traffic sign detection is realized in two basic parts. The first part is the detection of traffic sign edge which includes the segmentation by means of a large set of different thresholds in different colour spaces. For each input image a convenient threshold and colour component from one of colour spaces is chosen by the observation of the histogram of the whole image. Specially trained artificial neural network decides about the choice. In this way a system that is adaptable to different lighting conditions is obtained so that the segmentation is successfully carried out even in night lighting conditions where red colours prevail. After the segmentation in the first part the detection by means of the circular Hough transformation is carried out in larger radius range and that is why the system is adaptable to the different sizes of the traffic sign. The second part is the recognition where the most important part is the choice of real patterns and features. The first part, i.e. the detection module results in one or two best graded circles in the segmented image of the reduced resolution. Now, the suitable frame is within the original image and it is reduced to a certain dimension and is transfered into the grey image. The system automatically prepares the inputs for NN in accordance with so obtained interior of the traffic sign.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:168:877810
Study programme	Title: Postgraduate master programme in electrical engineering Study programme type: university Study level: postgraduate Academic / professional title: Magistar znanosti elektrotehnike (Magistar znanosti elektrotehnike)
Catalog URL	http://lib.fer.hr/cgi-bin/koha/opac-detail.pl?biblionumber=36749
Type of resource	Text
Extent	68 str ; 30 cm
File origin	Born digital
Access conditions	Closed access
Terms of use
Repository	FER Repository
Created on	2020-08-24 10:49:40

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