An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates
The detection of surface defects is very important for the quality improvement of steel plates. In actual production, as the steel plate production line runs faster, the steel surface defect detection algorithm is required to meet the requirements of real-time detection (less than 100 ms/image), and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/20/4222 |
| _version_ | 1819074388004175872 |
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| author | Yang Liu Ke Xu Jinwu Xu |
| author_facet | Yang Liu Ke Xu Jinwu Xu |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | The detection of surface defects is very important for the quality improvement of steel plates. In actual production, as the steel plate production line runs faster, the steel surface defect detection algorithm is required to meet the requirements of real-time detection (less than 100 ms/image), and the detection accuracy is improved (at least 90%). In this paper, an improved multi-block local binary pattern (LBP) algorithm is proposed. This algorithm not only has the simplicity and efficiency of the LBP algorithm, but also finds a suitable scale to describe the defect features by changing the block sizes, thus ensuring high recognition accuracy. The experiment proves that the method satisfies the requirements of online real-time detection in terms of speed (63 ms/image), and surpasses the widely-used scale invariant feature transform (SIFT), speeded up robust features (SURF), gray-level co-occurrence matrix (GLCM), and LBP algorithms in recognition accuracy (94.30%), which prove that the MB-LBP has practical application value in an online real-time detection system. |
| first_indexed | 2024-12-21T18:08:43Z |
| format | Article |
| id | doaj.art-7e8ed0430a8a42ff96db31a8a319dd98 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-21T18:08:43Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7e8ed0430a8a42ff96db31a8a319dd982022-12-21T18:54:51ZengMDPI AGApplied Sciences2076-34172019-10-01920422210.3390/app9204222app9204222An Improved MB-LBP Defect Recognition Approach for the Surface of Steel PlatesYang Liu0Ke Xu1Jinwu Xu2Collaborative Innovation Center of Steel Technology, University of Science and Technology, Beijing 100083, ChinaCollaborative Innovation Center of Steel Technology, University of Science and Technology, Beijing 100083, ChinaCollaborative Innovation Center of Steel Technology, University of Science and Technology, Beijing 100083, ChinaThe detection of surface defects is very important for the quality improvement of steel plates. In actual production, as the steel plate production line runs faster, the steel surface defect detection algorithm is required to meet the requirements of real-time detection (less than 100 ms/image), and the detection accuracy is improved (at least 90%). In this paper, an improved multi-block local binary pattern (LBP) algorithm is proposed. This algorithm not only has the simplicity and efficiency of the LBP algorithm, but also finds a suitable scale to describe the defect features by changing the block sizes, thus ensuring high recognition accuracy. The experiment proves that the method satisfies the requirements of online real-time detection in terms of speed (63 ms/image), and surpasses the widely-used scale invariant feature transform (SIFT), speeded up robust features (SURF), gray-level co-occurrence matrix (GLCM), and LBP algorithms in recognition accuracy (94.30%), which prove that the MB-LBP has practical application value in an online real-time detection system.https://www.mdpi.com/2076-3417/9/20/4222mb-lbpsurface defect detectionfeature extractiondefect recognition |
| spellingShingle | Yang Liu Ke Xu Jinwu Xu An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates Applied Sciences mb-lbp surface defect detection feature extraction defect recognition |
| title | An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates |
| title_full | An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates |
| title_fullStr | An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates |
| title_full_unstemmed | An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates |
| title_short | An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates |
| title_sort | improved mb lbp defect recognition approach for the surface of steel plates |
| topic | mb-lbp surface defect detection feature extraction defect recognition |
| url | https://www.mdpi.com/2076-3417/9/20/4222 |
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