Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1128122

Recognition of maximal speed limit traffic signs for use in advanced ADAS algorithms


Strišković, Barbara; Vranješ, Mario; Vranješ, Denis; Popović, Miroslav
Recognition of maximal speed limit traffic signs for use in advanced ADAS algorithms // 2021 Zooming Innovation in Consumer Technologies Conference (ZINC)
Novi Sad, 2021. str. 21-26 doi:10.1109/ZINC52049.2021.9499300 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1128122 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Recognition of maximal speed limit traffic signs for use in advanced ADAS algorithms

Autori
Strišković, Barbara ; Vranješ, Mario ; Vranješ, Denis ; Popović, Miroslav

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2021 Zooming Innovation in Consumer Technologies Conference (ZINC) / - Novi Sad, 2021, 21-26

ISBN
978-1-6654-0417-4

Skup
Zooming Innovation in Consumer Technologies Conference (ZINC 2021)

Mjesto i datum
Novi Sad, Srbija, 26.05.2021. - 27.05.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
ADAS ; convolutional neural network ; deep learning ; traffic sign classification

Sažetak
Advanced Driver Assistance Systems (ADAS) have been increasingly developing, specifically in the last decade. One of such ADAS is that intended for traffic signs recognition. This paper deals with the recognition of a specific subset of traffic signs, i.e. speed limit traffic signs. The complete solution is based on the usage of machine learning and finally implemented in the C programming language. After the optimization process, the final solution is implemented on the real ADAS board, to check its performance in a real operational environment. Due to the limited resources of the ADAS board itself, a simple Convolutional Neural Network (CNN) was created to recognize speed limit traffic signs. For CNN training a large database of 6891 training images is used. When testing the solution, 731 test images from the real traffic are used, as well as 123 real video sequences. The test results show that in certain situations the proposed solution is capable of achieving high performance in terms of precision, while in some cases additional improvements of the solution should be investigated. It is capable of processing 12 frames per second when operating with state-of- the-art automotive camera resolution, i.e. 1280x720 pixels.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekti:
DGS-UNIOS-ZUP 2018-6 - Povećavanje razine pouzdanosti vožnje autonomnih vozila pomoću sustava kamera na vozilu (Vranješ, Mario, DGS - Interni natječaja Sveučilišta Josipa Jurja Strossmayera u Osijeku za znanstvenoistraživačke i umjetničke projekte UNIOS-ZUP 2018) ( CroRIS)

Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Profili:

Avatar Url Mario Vranješ (autor)

Avatar Url Denis Vranješ (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Strišković, Barbara; Vranješ, Mario; Vranješ, Denis; Popović, Miroslav
Recognition of maximal speed limit traffic signs for use in advanced ADAS algorithms // 2021 Zooming Innovation in Consumer Technologies Conference (ZINC)
Novi Sad, 2021. str. 21-26 doi:10.1109/ZINC52049.2021.9499300 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Strišković, B., Vranješ, M., Vranješ, D. & Popović, M. (2021) Recognition of maximal speed limit traffic signs for use in advanced ADAS algorithms. U: 2021 Zooming Innovation in Consumer Technologies Conference (ZINC) doi:10.1109/ZINC52049.2021.9499300.
@article{article, author = {Stri\v{s}kovi\'{c}, Barbara and Vranje\v{s}, Mario and Vranje\v{s}, Denis and Popovi\'{c}, Miroslav}, year = {2021}, pages = {21-26}, DOI = {10.1109/ZINC52049.2021.9499300}, keywords = {ADAS, convolutional neural network, deep learning, traffic sign classification}, doi = {10.1109/ZINC52049.2021.9499300}, isbn = {978-1-6654-0417-4}, title = {Recognition of maximal speed limit traffic signs for use in advanced ADAS algorithms}, keyword = {ADAS, convolutional neural network, deep learning, traffic sign classification}, publisherplace = {Novi Sad, Srbija} }
@article{article, author = {Stri\v{s}kovi\'{c}, Barbara and Vranje\v{s}, Mario and Vranje\v{s}, Denis and Popovi\'{c}, Miroslav}, year = {2021}, pages = {21-26}, DOI = {10.1109/ZINC52049.2021.9499300}, keywords = {ADAS, convolutional neural network, deep learning, traffic sign classification}, doi = {10.1109/ZINC52049.2021.9499300}, isbn = {978-1-6654-0417-4}, title = {Recognition of maximal speed limit traffic signs for use in advanced ADAS algorithms}, keyword = {ADAS, convolutional neural network, deep learning, traffic sign classification}, publisherplace = {Novi Sad, Srbija} }

Citati:





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font