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Research Progress of Visual Inspection Technology of Steel Products—A Review
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The automation and intellectualization of the manufacturing processes in the iron and steel industry needs the strong support of inspection technologies, which play an important role in the field of quality control. At present, visual inspection technology based on image processing has an absolute advantage because of its intuitive nature, convenience, and efficiency. A major breakthrough in this field can be achieved if sufficient research regarding visual inspection technologies is undertaken. Therefore, the purpose of this article is to study the latest developments in steel inspection relating to the detected object, system hardware, and system software, existing problems of current inspection technologies, and future research directions. The paper mainly focuses on the research status and trends of inspection technology. The network framework based on deep learning provides space for the development of end-to-end mode inspection technology, which would greatly promote the implementation of intelligent manufacturing.
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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 [30]
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Suresh, V. V. N. S., Kalyani, Y., & Kumar, C. A. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-981-99-1288-9_38
CHALLENGES FACED BY SMEs : A COMPREHENSIVE REVIEW OF NON-DESTRUCTIVE TESTING METHODS AND SOLUT
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Bruce, D., Sanghvi, H. A., Behara, R. S., & Agarwal, A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4617143
Kumar, S. P., & Balasubramanian, N. V. (2024) · Journal of Advanced Manufacturing Systems · doi:10.1142/s0219686724500392
Stephani, H., Weibel, T., Rösch, R., & Moghiseh, A. (2024) · Lecture notes in statistics · doi:10.1007/978-3-031-66253-9_2
Metadata
- Authors
- Sun, X., Gu, J., Tang, S., & Li, J.
- Affiliations
- Jiangsu University (CN)
- Corresponding author
- Gu, Jinan
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2018-11-08
- Volume
- 8
- Issue
- 11
- Pages
- 2195
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W2900198492
- Cited by
- 109
- References
- 87
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 12 · 2023: 21 · 2022: 17 · 2021: 16 · 2020: 14 · 2019: 12
- FWCI
- 8.71
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (No.51875266)
- 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 inspection, steel products, image processing, quality control, deep learning, smart manufacturing, inspection software
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