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Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
Open accessAbstract
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.
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“However, these surveys paid more attention to the detection methods, and they simply summarized the research progress of the classification methods through supervised and unsupervised classifiers.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [31]
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On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning
Odeyemi, J., Ogbeyemi, A., Zhang, W., & Wong, K. (2024) · Bioengineering · doi:10.3390/bioengineering11020108 · Cited by 17
Yang, Z., Zhang, M., Li, C., Meng, Z., Li, Y., Chen, Y., & Liu, L. (2022) · IEEE Access · doi:10.1109/access.2022.3178380 · Cited by 17
Peña, D. G., Pérez, D. G., Díaz, I., & Juárez, J. M. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14069-7 · Cited by 17
DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM
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Defect detection in 3D-printed polymer parts using deep learning models: a comparative investigation
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Metadata
- Authors
- Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C. M., & Dario, P.
- Affiliations
- Scuola Superiore Sant'Anna (IT)
- Corresponding author
- Czimmermann, Tamás
- Oddo, Calogero Maria
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-03-06
- Volume
- 20
- Issue
- 5
- Pages
- 1459
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7085592
- OpenAlex
- W3009635072
- Cited by
- 441
- References
- 220
- Citations by year
- 2026: 74 · 2025: 81 · 2024: 82 · 2023: 72 · 2022: 72 · 2021: 42 · 2020: 18
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
- Funders
- Regione Toscana (CENTAURO project (CUP D88C15000210008))
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- visual defect detection, defect classification, industrial inspection, metal defects, ceramic defects, fabric defects, texture analysis, statistical texture analysis, supervised classifiers, deep learning
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