Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
Detecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA...
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MDPI AG
2025-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8759 |
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| author | Miao Peng Sue Bai Yang Lu |
| author_facet | Miao Peng Sue Bai Yang Lu |
| author_sort | Miao Peng |
| collection | DOAJ |
| description | Detecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA is integrated into the backbone and neck sections to reduce noise during gradient descent and enhance model stability by encoding global information and weighting model parameters. Second, the weighted fusion splicing module, Concat_BiFPN, is used in the neck network to facilitate multi-scale feature detection and fusion. This improves detection precision. The results show that the EB-YOLOv8 model increases detection accuracy on the NEU-DET dataset by 3.1%, reaching 80.2%, compared to YOLOv8. Additionally, the average precision on the Severstal steel defect dataset improves from 65.4% to 66.1%. Overall, the proposed model demonstrates superior recognition performance. |
| format | Article |
| id | doaj-art-14bb01ed082b4ff8828cd091b7a7b83a |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-14bb01ed082b4ff8828cd091b7a7b83a2025-08-20T03:36:02ZengMDPI AGApplied Sciences2076-34172025-08-011515875910.3390/app15158759Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 ModelingMiao Peng0Sue Bai1Yang Lu2Jilin Provincial Key Laboratory for Numerical Simulation, Jilin Normal University, Siping 136000, ChinaJilin Provincial Key Laboratory for Numerical Simulation, Jilin Normal University, Siping 136000, ChinaJilin Provincial Key Laboratory for Numerical Simulation, Jilin Normal University, Siping 136000, ChinaDetecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA is integrated into the backbone and neck sections to reduce noise during gradient descent and enhance model stability by encoding global information and weighting model parameters. Second, the weighted fusion splicing module, Concat_BiFPN, is used in the neck network to facilitate multi-scale feature detection and fusion. This improves detection precision. The results show that the EB-YOLOv8 model increases detection accuracy on the NEU-DET dataset by 3.1%, reaching 80.2%, compared to YOLOv8. Additionally, the average precision on the Severstal steel defect dataset improves from 65.4% to 66.1%. Overall, the proposed model demonstrates superior recognition performance.https://www.mdpi.com/2076-3417/15/15/8759YOLOv8object detectionattention mechanismBiFPNfeature fusion |
| spellingShingle | Miao Peng Sue Bai Yang Lu Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling Applied Sciences YOLOv8 object detection attention mechanism BiFPN feature fusion |
| title | Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling |
| title_full | Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling |
| title_fullStr | Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling |
| title_full_unstemmed | Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling |
| title_short | Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling |
| title_sort | steel surface defect detection algorithm based on improved yolov8 modeling |
| topic | YOLOv8 object detection attention mechanism BiFPN feature fusion |
| url | https://www.mdpi.com/2076-3417/15/15/8759 |
| work_keys_str_mv | AT miaopeng steelsurfacedefectdetectionalgorithmbasedonimprovedyolov8modeling AT suebai steelsurfacedefectdetectionalgorithmbasedonimprovedyolov8modeling AT yanglu steelsurfacedefectdetectionalgorithmbasedonimprovedyolov8modeling |