A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection
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Published:2023-09-25
Issue:10
Volume:9
Page:193
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ISSN:2313-433X
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Container-title:Journal of Imaging
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language:en
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Short-container-title:J. Imaging
Author:
Cumbajin Esteban1ORCID, Rodrigues Nuno1ORCID, Costa Paulo1, Miragaia Rolando1ORCID, Frazão Luís1ORCID, Costa Nuno1ORCID, Fernández-Caballero Antonio23ORCID, Carneiro Jorge4, Buruberri Leire H.4, Pereira António15ORCID
Affiliation:
1. Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal 2. Instituto de Investigación en Informática de Albacete, 02071 Albacete, Spain 3. Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain 4. Grestel-Produtos Cerâmicos S.A, Zona Industrial de Vagos-Lote 78, 3840-385 Vagos, Portugal 5. INOV INESC Inovação, Institute of New Technologies, Leiria Office, 2411-901 Leiria, Portugal
Abstract
Surface defect detection with machine learning has become an important tool in industries and a large field of study for researchers or workers in recent years. It is necessary to have a simplified source of information that helps us to better focus on one type of surface. In this systematic review, we present a classification for surface defect detection based on convolutional neural networks (CNNs) focused on surface types. Findings: Out of 253 records identified, 59 primary studies were eligible. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed the structures of each study and the concepts related to defects and their types on surfaces. The presented review is mainly focused on finding a classification for the types of surfaces most used in industry (metal, building, ceramic, wood, and special). We delve into the specifics of each surface category, offering illustrative examples of their applications within both industrial and laboratory settings. Furthermore, we propose a new taxonomy of machine learning based on the obtained results and collected information. We summarized the studies and extracted the main characteristics such as type of surface, problem types, timeline, type of network, techniques, and datasets. Among the most relevant results of our analysis, we found that the metallic surface is the most used, as it is the one found in 62.71% of the studies, and the most prevalent problem type is classification, accounting for 49.15% of the total. Furthermore, we observe that transfer learning was employed in 83.05% of the studies, while data augmentation was utilized in 59.32%. Our findings also provide insights into the cameras most frequently employed, along with the strategies adopted to address illumination challenges present in certain articles and the approach to creating datasets for real-world applications. The main results presented in this review allow for a quick and efficient search of information for researchers and professionals interested in improving the results of their defect detection projects. Finally, we analyzed the trends that could open new fields of study for future research in the area of surface defect detection.
Funder
project STC 4.0 HP Portuguese Fundação para a Ciência e a Tecnologia—FCT Portuguese national funds iRel40
Subject
Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging
Reference97 articles.
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