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Technology transfer of computer vision defect detection to ceramic tiles industry


Hocenski, Željko; Matić, Tomislav; Vidović, Ivan
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: Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2016. str. 301-305 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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 : Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 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, 12-14.10.2016

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