Surface Defect Detection of Rolled Steel Based on Lightweight Model
A lightweight rolled steel strip surface defect detection model, YOLOv5s-GCE, is proposed to improve the efficiency and accuracy of industrialized rolled steel strip defect detection. The Ghost module is used to replace the CBS structure in a part of the original YOLOv5s model, and the Ghost bottlen...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/17/8905 |
| _version_ | 1797496144026140672 |
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| author | Shunyong Zhou Yalan Zeng Sicheng Li Hao Zhu Xue Liu Xin Zhang |
| author_facet | Shunyong Zhou Yalan Zeng Sicheng Li Hao Zhu Xue Liu Xin Zhang |
| author_sort | Shunyong Zhou |
| collection | DOAJ |
| description | A lightweight rolled steel strip surface defect detection model, YOLOv5s-GCE, is proposed to improve the efficiency and accuracy of industrialized rolled steel strip defect detection. The Ghost module is used to replace the CBS structure in a part of the original YOLOv5s model, and the Ghost bottleneck is employed to replace the bottleneck structure in C3 to minimize the model’s size and make the network lightweight. The EIoU function is added to improve the accuracy of the regression of the prediction frame and accelerate its convergence. The CA (Coordinate Attention) attention method is implemented to reinforce critical feature channels and their position information, enabling the model to identify and find targets correctly. The experimental results demonstrate that the accuracy of YOLOv5s-GCE is 85.7%, which is 3.5% higher than that of the original network; the model size is 7.6 MB, which is 44.9% smaller than that of the original network; the number of model parameters and calculations are reduced by 47.1% and 48.8%, respectively; and the detection speed reached 58.8 fps. YOLOv5s-GCE meets the necessity for real-time identification of rolled steel flaws in industrial production compared to other common algorithms. |
| first_indexed | 2024-03-10T01:59:29Z |
| format | Article |
| id | doaj.art-0e9960fea86b4f9aa6601e4aac35ad34 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:59:29Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0e9960fea86b4f9aa6601e4aac35ad342023-11-23T12:48:51ZengMDPI AGApplied Sciences2076-34172022-09-011217890510.3390/app12178905Surface Defect Detection of Rolled Steel Based on Lightweight ModelShunyong Zhou0Yalan Zeng1Sicheng Li2Hao Zhu3Xue Liu4Xin Zhang5School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaA lightweight rolled steel strip surface defect detection model, YOLOv5s-GCE, is proposed to improve the efficiency and accuracy of industrialized rolled steel strip defect detection. The Ghost module is used to replace the CBS structure in a part of the original YOLOv5s model, and the Ghost bottleneck is employed to replace the bottleneck structure in C3 to minimize the model’s size and make the network lightweight. The EIoU function is added to improve the accuracy of the regression of the prediction frame and accelerate its convergence. The CA (Coordinate Attention) attention method is implemented to reinforce critical feature channels and their position information, enabling the model to identify and find targets correctly. The experimental results demonstrate that the accuracy of YOLOv5s-GCE is 85.7%, which is 3.5% higher than that of the original network; the model size is 7.6 MB, which is 44.9% smaller than that of the original network; the number of model parameters and calculations are reduced by 47.1% and 48.8%, respectively; and the detection speed reached 58.8 fps. YOLOv5s-GCE meets the necessity for real-time identification of rolled steel flaws in industrial production compared to other common algorithms.https://www.mdpi.com/2076-3417/12/17/8905defect detectionghostattention mechanismEIoU |
| spellingShingle | Shunyong Zhou Yalan Zeng Sicheng Li Hao Zhu Xue Liu Xin Zhang Surface Defect Detection of Rolled Steel Based on Lightweight Model Applied Sciences defect detection ghost attention mechanism EIoU |
| title | Surface Defect Detection of Rolled Steel Based on Lightweight Model |
| title_full | Surface Defect Detection of Rolled Steel Based on Lightweight Model |
| title_fullStr | Surface Defect Detection of Rolled Steel Based on Lightweight Model |
| title_full_unstemmed | Surface Defect Detection of Rolled Steel Based on Lightweight Model |
| title_short | Surface Defect Detection of Rolled Steel Based on Lightweight Model |
| title_sort | surface defect detection of rolled steel based on lightweight model |
| topic | defect detection ghost attention mechanism EIoU |
| url | https://www.mdpi.com/2076-3417/12/17/8905 |
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