Pregled bibliografske jedinice broj: 1028030
Classification of Objects Detected by the Camera based on Convolutional Neural Network
Classification of Objects Detected by the Camera based on Convolutional Neural Network // 2019 Zooming Innovation in Consumer Technologies Conference (ZINC)
Novi Sad, Srbija: Institute of Electrical and Electronics Engineers (IEEE), 2019. str. 113-117 doi:10.1109/ZINC.2019.8769392 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1028030 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Classification of Objects Detected by the Camera based on Convolutional Neural Network
Autori
Kulić, Filip ; Grbić, Ratko ; Todorović, Branislav M. ; Anđelić, Tihomir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2019 Zooming Innovation in Consumer Technologies Conference (ZINC)
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2019, 113-117
Skup
Zooming Innovation in Consumer Technologies Conference (ZINC 2019)
Mjesto i datum
Novi Sad, Srbija, 29.05.2019. - 30.05.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
ADAS ; image classification ; convolutional neural network
Sažetak
Nowadays, we are trying to achieve as much vehicle autonomy as possible by developing Advanced Driver-Assistance Systems (ADAS). For such a system to make decisions, it should have insight into the environment of the vehicle, e.g. the objects surrounding the vehicle. During forward driving, the information about the objects in front of the vehicle is usually obtained by a front view in-vehicle camera. This paper describes the image classification method of the objects in the front of the vehicle based on deep convolutional neural networks (CNN). Such CNN is supposed to be implemented in embedded system of an autonomous vehicle and the inference should satisfy real-time constraints. This means that the CNN should be structured to have fast inference by reducing the number of operations as much as possible, but still having satisfying accuracy. This can be achieved by reducing the number of parameters which also means that the resulting network has lower memory requirements. This paper describes the process of realizing such a network, from image dataset development up to the CNN structuring and training. The proposed CNN is compared to the state-of-the-art deep neural network in terms of classification accuracy, inference speed and memory requirements.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
UNIOS-ZUP 2018-6
Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek
Profili:
Ratko Grbić
(autor)