Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Autor: Tamás Czimmermann, Gastone Ciuti, Mario Milazzo, Marcello Chiurazzi, Stefano Roccella, Calogero Maria Oddo, Paolo Dario
Jazyk: angličtina
Rok vydání: 2020
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Zdroj: Sensors, Vol 20, Iss 5, p 1459 (2020)
Druh dokumentu: article
ISSN: 1424-8220
DOI: 10.3390/s20051459
Popis: This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.
Databáze: Directory of Open Access Journals
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