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Pregled bibliografske jedinice broj: 1028030

Classification of Objects Detected by the Camera based on Convolutional Neural Network


Kulić, Filip; Grbić, Ratko; Todorović, Branislav M.; Anđelić, Tihomir
Classification of Objects Detected by the Camera based on Convolutional Neural Network // 2019 Zooming Innovation in Consumer Technologies Conference (ZINC)
Novi Sad, Srbija: IEEE, 2019. str. 113-117 doi:10.1109/ZINC.2019.8769392 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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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) / - : IEEE, 2019, 113-117

Skup
2019 Zooming Innovation in Consumer Electronics International Conference

Mjesto i datum
Novi Sad, Srbija, 29-30.05.2019

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:

Avatar Url Ratko Grbić (autor)

Citiraj ovu publikaciju

Kulić, Filip; Grbić, Ratko; Todorović, Branislav M.; Anđelić, Tihomir
Classification of Objects Detected by the Camera based on Convolutional Neural Network // 2019 Zooming Innovation in Consumer Technologies Conference (ZINC)
Novi Sad, Srbija: IEEE, 2019. str. 113-117 doi:10.1109/ZINC.2019.8769392 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Kulić, F., Grbić, R., Todorović, B. & Anđelić, T. (2019) Classification of Objects Detected by the Camera based on Convolutional Neural Network. U: 2019 Zooming Innovation in Consumer Technologies Conference (ZINC) doi:10.1109/ZINC.2019.8769392.
@article{article, year = {2019}, pages = {113-117}, DOI = {10.1109/ZINC.2019.8769392}, keywords = {ADAS, image classification, convolutional neural network}, doi = {10.1109/ZINC.2019.8769392}, title = {Classification of Objects Detected by the Camera based on Convolutional Neural Network}, keyword = {ADAS, image classification, convolutional neural network}, publisher = {IEEE}, publisherplace = {Novi Sad, Srbija} }
@article{article, year = {2019}, pages = {113-117}, DOI = {10.1109/ZINC.2019.8769392}, keywords = {ADAS, image classification, convolutional neural network}, doi = {10.1109/ZINC.2019.8769392}, title = {Classification of Objects Detected by the Camera based on Convolutional Neural Network}, keyword = {ADAS, image classification, convolutional neural network}, publisher = {IEEE}, publisherplace = {Novi Sad, Srbija} }

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