Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
By Tamás Czimmermann, Gastone Ciuti, Mario Milazzo, Marcello Chiurazzi, Stefano Roccella, Calogero Maria Oddo, Paolo Dario. Italian National Conference on Sensors, 2020
369 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Media Technology
View Paper: https://www.semanticscholar.org/paper/f11d69fac609917da82f697758cd694c0f01a730
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In Computer Vision and Pattern Recognition
In Industrial and Manufacturing Engineering
Citations in the corpus
- Deep Industrial Image Anomaly Detection: A Survey (2023, background): “Czimmermann [1] Tao [2] Cui [ 1 demonstrates clearly the merits of our survey in terms of dataset, metric, neural network architecture, levels of supervision and promising setting for industrial manufacturing.”