Pregled bibliografske jedinice broj: 1186063
Weather Condition Classification in Vehicle Environment Based on Front-View Camera Images
Weather Condition Classification in Vehicle Environment Based on Front-View Camera Images // 2022 21st International Symposium INFOTEH-JAHORINA (INFOTEH)
Sarajevo, 2022. str. 1-4 doi:10.1109/INFOTEH53737.2022.9751279 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1186063 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Weather Condition Classification in Vehicle
Environment Based on Front-View Camera Images
Autori
Triva, Jakob ; Grbić, Ratko ; Vranješ, Mario ; Teslić, Nikola
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2022 21st International Symposium INFOTEH-JAHORINA (INFOTEH)
/ - Sarajevo, 2022, 1-4
Skup
21st International Symposium INFOTEH-JAHORINA
Mjesto i datum
Jahorina, Bosna i Hercegovina, 16.03.2022. - 18.03.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
weather conditions detection ; autonomous driving ; convolutional neural network ; front-view camera
Sažetak
The current environmental conditions should be monitored during autonomous driving since the different weather conditions can have a different impact on implemented sensor system or on the efficiency of the implemented control system. In this paper, the classification of weather conditions in the vehicle environment is based on images captured by a front-view camera, which are further processed by the simple Convolutional Neural Network (CNN). For model development purposes, training and validation data sets were created from two sources: the BDD100K database and by extracting frames from the collected video sequences. The solution implements an additional mechanism to filter out false predictions based on a circular buffer. The proposed solution achieves the F1 measure of 98.3% for the entire test video frames data set, where it achieves the best results in snowy weather detection (Precision of 100%, F1 of 100.00%) and the worst in foggy weather detection (Precision of 97.25%, F1 of 98.00%).
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
