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Pregled bibliografske jedinice broj: 1090597

Traffic Sign Detection Using YOLOv3


Mijić, David; Brisinello, Matteo; Vranješ, Mario; Grbić, Ratko
Traffic Sign Detection Using YOLOv3 // Proceedings of 10TH IEEE INTERNATIONAL CONFERENCE ON CONSUMER TECHNOLOGY
Berlin, 2020. str. 1-6 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Traffic Sign Detection Using YOLOv3

Autori
Mijić, David ; Brisinello, Matteo ; Vranješ, Mario ; Grbić, Ratko

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

Izvornik
Proceedings of 10TH IEEE INTERNATIONAL CONFERENCE ON CONSUMER TECHNOLOGY / - Berlin, 2020, 1-6

Skup
10TH IEEE INTERNATIONAL CONFERENCE ON CONSUMER TECHNOLOGY

Mjesto i datum
Berlin, Njemačka, 9-12.11.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
ADAS ; traffic sign detection ; YOLO ; deep learning

Sažetak
Advanced driving assistance systems (ADASs) are increasingly being installed in modern vehicles because they make driving safer and more comfortable. With the implementation of cameras in the vehicle, the range of possible ADASs increases. One of such systems is the one aimed for traffic sign recognition, which alerts the driver about different road conditions such as excess of the speed limit or traffic ban. In this paper, a solution for detecting a specific set of 11 traffic signs typical for most European countries is presented. The algorithm used for detecting traffic signs is You Only Look Once (YOLO) v3, where the model parameters are trained on a train set acquired from the newly created dataset. The rest of the dataset images are used for creating a test set. The dataset is derived from the video signals that were capturing traffic with a front view camera mounted inside the vehicle, in the city of Osijek in different weather conditions (sunny, cloudy, rain, night). The dataset images are extracted from 28 different video sequences, which resulted in 5567 images with the total number of 6751 annotated traffic signs. The proposed solution for detecting a specific set of traffic signs achieves high performance when tested on the test set created from the proposed dataset.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Profili:

Avatar Url Ratko Grbić (autor)

Avatar Url Mario Vranješ (autor)


Citiraj ovu publikaciju

Mijić, David; Brisinello, Matteo; Vranješ, Mario; Grbić, Ratko
Traffic Sign Detection Using YOLOv3 // Proceedings of 10TH IEEE INTERNATIONAL CONFERENCE ON CONSUMER TECHNOLOGY
Berlin, 2020. str. 1-6 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Mijić, D., Brisinello, M., Vranješ, M. & Grbić, R. (2020) Traffic Sign Detection Using YOLOv3. U: Proceedings of 10TH IEEE INTERNATIONAL CONFERENCE ON CONSUMER TECHNOLOGY.
@article{article, year = {2020}, pages = {1-6}, keywords = {ADAS, traffic sign detection, YOLO, deep learning}, title = {Traffic Sign Detection Using YOLOv3}, keyword = {ADAS, traffic sign detection, YOLO, deep learning}, publisherplace = {Berlin, Njema\v{c}ka} }
@article{article, year = {2020}, pages = {1-6}, keywords = {ADAS, traffic sign detection, YOLO, deep learning}, title = {Traffic Sign Detection Using YOLOv3}, keyword = {ADAS, traffic sign detection, YOLO, deep learning}, publisherplace = {Berlin, Njema\v{c}ka} }




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