Pregled bibliografske jedinice broj: 839521
Technology transfer of computer vision defect detection to ceramic tiles industry
Technology transfer of computer vision defect detection to ceramic tiles industry // PROCEEDINGS OF 2016 International Conference on Smart Systems and Technologies (SST) / Drago Žagar, Goran Martinović, Snježana Rimac Drlje (ur.).
Osijek: Fakultet elektrotehnike, računarstva i informacijskih tehnologija Sveučilišta Josipa Jurja Strossmayera u Osijeku, 2016. str. 301-305 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 839521 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Technology transfer of computer vision defect detection to ceramic tiles industry
Autori
Hocenski, Željko ; Matić, Tomislav ; Vidović, Ivan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
PROCEEDINGS OF 2016 International Conference on Smart Systems and Technologies (SST)
/ Drago Žagar, Goran Martinović, Snježana Rimac Drlje - Osijek : Fakultet elektrotehnike, računarstva i informacijskih tehnologija Sveučilišta Josipa Jurja Strossmayera u Osijeku, 2016, 301-305
ISBN
978-1-5090-3718-6
Skup
2016 International Conference on Smart Systems and Technologies (SST 2016)
Mjesto i datum
Osijek, Hrvatska, 12.10.2016. - 14.10.2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
biscuit ceramic tile ; computer vision station ; defect detection ; prototype ; real-time
Sažetak
Visual inspection is carried out manually in ceramic tile industry in Croatia using specially trained and skilled workers. Currently in industry biscuit and crude tiles are not visually inspected for defects. Fatigue, illness and other subjective factors significantly influence workers percentage of found defects and classification quality. In this paper we present a prototype computer vision station (CVS) for real-time biscuit tile defects detection. CVS is a result of an FP7 project. Prototype is mounted on a production conveyor line before the kiln. MFC (Microsoft Foundation Class) based GUI application is created and all developed algorithms are implemented in C++ language using OpenCV and Nvidia CUDA libraries. System hardware is based on core i7 CPU and Nvidia GTX960 GPU. Preliminary results show maximum execution time below 900 ms and defect detection efficiency of 98%.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek