Design of online surface inspection system of hot rolled strips
Abstract
An online surface inspection system of hot rolled strips is introduced. This system is designed to detect such main surface defects on hot rolled strips as scar, scratches, pits, cracks, cross hatchings, and so on. Multiple CCD area scan cameras are adopted to capture images of strip surface simultaneously, and all the images are processed by parallel computation system real-time, which is supported by fast image process techniques and parallel computation techniques, in order to snap main defect regions on the surface of strips. At last, the defects will be classified to several types. The application of the system to a practical production line shows that it can detect main defects of hot rolled strips more effectively than traditional method, and it is easily to be maintained.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
There are many examples of use of two sets of cameras covering both bright and dark fields of view.
“There are many examples of the use of two sets of cameras covering both the fields of view [24-26].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [24]
Use of 20 CCD area scan cameras to capture surface image of both sides of hot-rolled strips using both bright field and dark field modes has been reported in an iron and steel plant of China.
“Use of 20 charge-coupled device (CCD) area scan cameras which are used to capture surface image of both sides of hot-rolled strips using both bright field and dark field modes have been reported in an iron and steel plant of China [24].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [24]
Images captured by a CCD camera are transferred to some form of fast, parallel processing system dedicated to the camera and located close to it.
“Images captured by a CCD camera are transferred to some form of fast, parallel processing system dedicated to the camera and located close to it [24].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [24]
Cross web image resolutions vary from 0.17 mm to about 1 mm while reported down-web resolutions vary from 0.25 to 1.25 mm.
“Cross web resolutions vary from 0.17 mm to about 1 mm while reported down-web resolutions vary from 0.25 to 1.25 mm.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [24]
Spatial domain techniques are fast and easy to implement compared to wavelet-based methods, and they have been used in most papers where suitability for real-time operation has been reported.
“Spatial domain techniques are fast and easy to implement compared to wavelet-based methods, and they have been used in most of the papers where suitability for real-time operation have also been reported [2,24,26,35,46,58,59,61,76].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [24]
Region growing has been employed, particularly after use of gradient operators, to identify RoIs.
“Region growing has been employed [2,24,59,76], particularly after the use of gradient operators, to identify RoIs.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [24]
Background difference method has found favour with researchers.
“Background difference method has also found favour with researchers [24,31,56].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [24]
Thresholding methods are widely applied in online AVI systems.
“Thresholding methods are usually used to separate the defective regions on flat steel surfaces, which have been widely applied in online AVI systems [19,20].”
From Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020) · cites this paper as [19]
Guifang Wu achieved 88% accuracy on 4 classes with -40 ms execution time.
“Guifang Wu. [6] -40 ms 88% 4”
From Real-time system for automatic cold strip surface defect detection (2019) · cites this paper as [6]
Cites
A comparison of methods for multiclass support vector machines
Hsu, C., & Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,861 · see citing works
Diffuse and Specular Reflectance from Rough Surfaces
van Ginneken, B., Stavridi, M., & Koenderink, J. J. (1998) · Applied Optics · doi:10.1364/ao.37.000130 · Cited by 209 · see citing works
Determining reflectance properties of an object using range and brightness images
Ikeuchi, K., & Sato, K. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.103274 · Cited by 117 · see citing works
Analysis of the electrical properties of PZT by a BP artificial neural network
Cai, K., Xia, J., Li, L., et al. (2005) · Computational Materials Science · doi:10.1016/j.commatsci.2004.12.066 · Cited by 37 · see citing works
SUGIMOTO, T., & Kawaguchi, T. (1998) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/19.744183 · Cited by 25 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Automated Visual Defect Detection for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2963555 · Cited by 559 · see citing works
Review of vision-based steel surface inspection systems
Neogi, N., Mohanta, D. K., & Dutta, P. K. (2014) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2014-50 · Cited by 322 · see citing works
“Background difference method has also found favour with researchers [24,31,56].”
BackgroundMethodResultInfluentialResearch Progress of Visual Inspection Technology of Steel Products—A Review
Sun, X., Gu, J., Tang, S., & Li, J. (2018) · Applied Sciences · doi:10.3390/app8112195 · Cited by 109 · see citing works
Wang, Y., Xia, H., Yuan, X., Li, L., & Sun, B. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5238-0 · Cited by 61 · see citing works
An evolutionary classifier for steel surface defects with small sample set
Xiao, M., Jiang, M., Li, G., Xie, L., & Yi, L. (2017) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-017-0197-y · Cited by 55 · see citing works
Defect detection of steel surface using entropy segmentation
Nand, G. K., Noopur, & Neogi, N. (2014) · 2014 Annual IEEE India Conference (INDICON) · doi:10.1109/indicon.2014.7030439 · Cited by 44 · see citing works
Real-time system for automatic cold strip surface defect detection
Kostenetskiy, P., Alkapov, R., Vetoshkin, N., Chulkevich, R., Napolskikh, I., & Poponin, O. (2019) · FME Transactions · doi:10.5937/fmet1904765k · Cited by 23 · see citing works
- Authors
- Wu, G., Kwak, H., Jang, S., Xu, K., & Xu, J.
- Affiliations
- Henan University of Science and Technology (CN)
- University of Science and Technology Beijing (CN)
- ORCID
- Venue
- 2008 IEEE International Conference on Automation and Logistics
- Publisher
- IEEE
- Published
- 2008-09
- Pages
- 2291-2295
- OpenAlex
- W2139176774
- Cited by
- 38
- References
- 5
- Citations by year
- 2025: 1 · 2024: 4 · 2022: 3 · 2021: 1 · 2020: 4 · 2019: 3 · 2018: 2 · 2017: 3 · 2016: 4 · 2014: 6 · 2013: 2 · 2012: 3
- FWCI
- 1.69
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Automation and Logistics (ICAL) (publishedVersion)
- 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
- hot-rolled strip, surface defect detection, parallel image processing, real-time image processing, defect classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
