The DOI matches its Crossref record; 211 of the 211 references it cites were found in a registry or an index.
State of the Art in Defect Detection Based on Machine Vision
Open accessAbstract
Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.
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Cited by
A deep learning model for steel surface defect detection
Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01180-7 · Cited by 122
Smart Machine Vision System to Improve Decision-Making on the Assembly Line
Silva, C. A. D. S., & Paladini, E. P. (2025) · Machines · doi:10.3390/machines13020098 · Cited by 13
Metadata
- Authors
- Ren, Z., Fang, F., Yan, N., & Wu, Y.
- Venue
- International Journal of Precision Engineering and Manufacturing-Green Technology
- Published
- 2021-05-26
- Volume
- 9
- Issue
- 2
- Pages
- 661-691
- ISSN
- 2198-0810 (ISSN-L)
- 2288-6206
- OpenAlex
- W3164104137
- Cited by
- 799
- References
- 211
- FWCI
- 50.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Ren, Z., Fang, F., Yan, N., & Wu, Y. (2022). State of the Art in Defect Detection Based on Machine Vision. International Journal of Precision Engineering and Manufacturing-Green Technology. https://doi.org/10.1007/s40684-021-00343-6
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