The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5
In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/5399616 |
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| author | Yiming Xu Ziheng Ding Wang Li Kai Zhang Le Tong |
| author_facet | Yiming Xu Ziheng Ding Wang Li Kai Zhang Le Tong |
| author_sort | Yiming Xu |
| collection | DOAJ |
| description | In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process. |
| format | Article |
| id | doaj-art-9f7ddff146c144989ffe07ff0b79a6e3 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-9f7ddff146c144989ffe07ff0b79a6e32025-08-20T03:38:30ZengWileyJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/5399616The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5Yiming Xu0Ziheng Ding1Wang Li2Kai Zhang3Le Tong4School of Electrical EngineeringSchool of Electrical EngineeringSchool of Electrical EngineeringSchool of Electrical EngineeringShanghai Normal UniversityIn the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process.http://dx.doi.org/10.1155/2023/5399616 |
| spellingShingle | Yiming Xu Ziheng Ding Wang Li Kai Zhang Le Tong The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5 Journal of Electrical and Computer Engineering |
| title | The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5 |
| title_full | The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5 |
| title_fullStr | The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5 |
| title_full_unstemmed | The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5 |
| title_short | The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5 |
| title_sort | steel surface multiple defect detection and size measurement system based on improved yolov5 |
| url | http://dx.doi.org/10.1155/2023/5399616 |
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