Nejc Kozamernik (Author), Drago Bračun (Author)

Abstract

Electric cathode metal coating (KTL) is a popular choice for surface protection of metal components in the automotive industry. Due to the complex 3D shape of the parts and the glossy black color of the coating, machine vision inspection is very sensitive to variabilities among parts and to the variabilities in their positioning during the image acquisition. In this paper a variational autoencoder model for anomaly detection is presented to make further image processing more immune to variability and to detect coating defects more reliably.

Keywords

KTL zaščita;iskanje površinskih napak;slikovni sistem;detekcija anomalij;globoki generativni model;variacijski avtoenkoder;surface defect inspection;imaging system;anomaly detection;deep generative models;variational autoencoders;

Data

Language: English
Year of publishing:
Typology: 1.08 - Published Scientific Conference Contribution
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 004.92:629.7(045)
COBISS: 32754691 Link will open in a new window
ISSN: 2212-8271
Views: 380
Downloads: 175
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary keywords: KTL zaščita;iskanje površinskih napak;slikovni sistem;detekcija anomalij;globoki generativni model;variacijski avtoenkoder;
Type (COBISS): Article
Pages: Str. 1558-1563
Issue: ǂVol. ǂ93
Chronology: 2020
DOI: 10.1016/j.procir.2020.04.114
ID: 12078846