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

The BioRobotics Institute of Scuola Superiore Sant’Anna and Department of Excellence in Robotics and AI of Scuola Superiore Sant’Anna, 56025 Pontedera (PISA), Italy
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Sensors 2020, 20(5), 1459; https://doi.org/10.3390/s20051459
Received: 9 February 2020 / Revised: 2 March 2020 / Accepted: 2 March 2020 / Published: 6 March 2020
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0)
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. View Full-Text
Keywords: defect detection; classification; deep learning; industry 4.0; survey defect detection; classification; deep learning; industry 4.0; survey
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MDPI and ACS Style

Czimmermann, T.; Ciuti, G.; Milazzo, M.; Chiurazzi, M.; Roccella, S.; Oddo, C.M.; Dario, P. Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY. Sensors 2020, 20, 1459. https://doi.org/10.3390/s20051459

AMA Style

Czimmermann T, Ciuti G, Milazzo M, Chiurazzi M, Roccella S, Oddo CM, Dario P. Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY. Sensors. 2020; 20(5):1459. https://doi.org/10.3390/s20051459

Chicago/Turabian Style

Czimmermann, Tamás, Gastone Ciuti, Mario Milazzo, Marcello Chiurazzi, Stefano Roccella, Calogero M. Oddo, and Paolo Dario. 2020. "Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY" Sensors 20, no. 5: 1459. https://doi.org/10.3390/s20051459

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