A computer vision system for automatic steel surface inspection
Abstract
Automatic inspection on line plays an important role in industrial quality management nowadays. This paper proposes a new computer vision system for automatic steel surface inspection. The system analyzes the images sequentially acquired from steel bar to detect different kinds of defects on the steel surface. Several image processing strategies are used to detect and outline the defects. The detected defects are then classified into different defect types by using a hierarchical neural network classifier. Some manual detection results by field experts are used to verify the correctness of the proposed detection. In defect classification, the results show that the relevance vector machine (RVM) has better accuracy than the back propagation neural network (BPN). The proposed algorithm was found capable of detecting defects on steel surface rapidly and precisely.
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Averaging filter and Gaussian filter are used routinely before processing for defect detection.
“Averaging filter [58,61] and Gaussian filter [59] are used routinely before processing for defect detection.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [59]
At the beginning of image processing, bar image is recovered from the background for better outcome of defect segmentation.
“At the beginning of image processing, bar image is recovered from the background for better outcome of defect segmentation [5,44,58,59].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [59]
First-order gradient filter followed by thresholding is more commonly used for edge localisation due to its noise immunity characteristics compared to Laplacian.
“First-order gradient filter followed by thresholding is more commonly used for edge localisation [2,44,46,59,76] due to its noise immunity characteristics compared to Laplacian.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [59]
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 [59]
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 [59]
Steel surfaces exhibit many types of defects, and NN-BP has been extensively used to classify such defects.
“Steel surfaces exhibit many types of defects, and NNBP has been extensively used to classify such defects [31,36,54,59,63,72,78,80].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [59]
Median filters have the best performance on the suppression of salt and pepper noise, and Gaussian filter and Wiener filter have better effects on Gaussian noise.
“Among them, it is proved that the median filters have the best performance on the suppression of salt and pepper noise, and the Gaussian filter and Wiener filter have better effects on Gaussian noise.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [49]
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References 8 · Cited by 23
- Authors
- Liu, Y.-C., Hsu, Y.-L., Sun, Y.-N., Tsai, S.-J., Ho, C.-Y., & Chen, C.-M.
- Affiliations
- National Cheng Kung University (TW)
- China Steel (Taiwan) (TW)
- Venue
- 2010 5th IEEE Conference on Industrial Electronics and Applications
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 1667-1670
- OpenAlex
- W2044722202
- Cited by
- 23
- References
- 8
- Citations by year
- 2024: 1 · 2022: 2 · 2021: 1 · 2020: 3 · 2018: 1 · 2017: 4 · 2016: 1 · 2015: 2 · 2014: 1 · 2013: 1 · 2012: 4
- FWCI
- 7.09
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Industrial Electronics and Applications (ICIEA) (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
- defect detection, steel bars, online inspection, relevance vector machine, BP neural network, defect classification, image processing, manufacturing quality control
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