20.500.12556/RUL-103535 Detekcija napak na materialih s periodično strukturo Detecting Defects in Materials That Exhibit Periodic Structure Razvili smo metodo strojnega vida, ki bi naj bila sposobna detektirati napake v materilalih, ki iskazujejo periodično strukturo. Metoda temelji na dvodimenzionalni Fourierjevi transformaciji. Naša osnovna predpostavka je, da pri Fourierjevi transformaciji slike vzorca, ki izkazuje periodično strukturo, dobimo veliko število vrhov, in majhno število vrhov, ko vzorec vsebuje napako. Vrhovi so zaznani z detektorjem MSER. Izhod detektorja MSER je število vrhov. Kot primer smo uporabili vzorce tekstila, v katere smo vnesli napake (cefranje, zbadanje, rezanje). Zajeli smo sistematično bazo slik, ter napake na tekstilnih vzorcih označili z zaključenimi poligoni. Na vsakem zaključenem poligonu smo izračunali očrtan pravokotnik, ki je bil vodilo, za izrez vsakega od vzorcev, ki smo jih uporabili za potrebe binarnega razvrščanja vzorcev na tiste brez napake in z napako. Za detekcijo napak smo kvalitativno ilustrirali delovanje detektorja napak, ki uporablja predstavljen razvrščevalnik ter metodo drsečega okna. We have developed a machine vision method that will be able to detect errors in materials that show a periodic structure. The method is based on a two-dimensional Fourier transform. Our basic assumption is that the Fourier transformation of the image that exhibits a periodic structure results in a pattern which contains a large number of peaks. Conversely, it contains a small number of peaks when the sample contains a defect, which disrupts the periodic structure. The peaks are detected by the MSER detector. The output of the MSER detector is the number of peaks. To illustrate and evaluate the proposed method, we used textile samples in which we created defects (tearing, puncturing, cutting). We systematically collected a database of images, and marked the defects in textile patterns as polygons. For each polygon we calculated the bounding box that was used in sample extraction from images. Samples were classified to those without defect and those containing a defect. For the detection of defects, we have qualitatively illustrated the operation of the error detector using the presented classifier and the sliding window method. strojni vid računalniški vid tekstil razvrščevanje detekcija napak machine vision computer vision textile classification defect detection true false false Slovenski jezik Angleški jezik Diplomsko delo/naloga 2018-09-19 11:00:10 2018-09-19 11:00:14 2022-08-16 03:42:42 0000-00-00 00:00:00 2018 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1970-01-01 42741 Krupic_Bilal_-_Detekcija_napak_na_materialih_s_periodicno_strukturo.pdf Krupic_Bilal_-_Detekcija_napak_na_materialih_s_periodicno_strukturo.pdf 1 51051A1EDDA7B2FE5A19AEC6C15F4C71 167afb08403d7a592b37f5e98759855364a6172dc54a4283e1c1b8def6263967 d56d1409-a1b5-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=113975 Fakulteta za elektrotehniko 0 0 0