| Autor: |
Ingrid Martins Valente Costa, Lorena Cândida Mendonça, Miguel Gonçalves de Freitas, Talles Marcelo Gonçalves de Andrade Barbosa, Symone Gomes Soares Alcalá |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
ITEGAM-JETIA, Vol 6, Iss 23 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2447-0228 |
| Popis: |
Vision systems have been widely employed in industries to automate the inspection process in products. Their use provides standardized, reliable and accurate inspections when compared to a human operator. Vision systems pass to machines the ability to view and automatically extract features in order to indicate abnormalities in products. This paper proposes a vision system for capturing and preprocessing digital images, besides classifying objects with defect and objects without defect using an Artificial Neural Network model. As a case study, digital images of boxes are acquired and classified on a conveyor belt. Tests reveal that the proposed system is able to classify accurately a box with defect and a box without defect in real time. The main contribution of this paper is the proposal of a system that performs automated inspections in products, in order to detect abnormalities, and it can be easily coupled, modularly, to the existing industrial platforms. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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