Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5
Abstract The roller is a crucial tool in the hot-rolled strip steel manufacturing process. Rapid and accurate detection of roll surface defects is essential for enhancing product surface quality, ensuring dimensional precision, and reducing scrap rates. Currently, industrial roll inspection primaril...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06920-7 |
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| _version_ | 1849329458661556224 |
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| author | Yulong Hu Wen Peng Shiyi Chen Jinyun Liu Xudong Li Jie Sun Dianhua Zhang |
| author_facet | Yulong Hu Wen Peng Shiyi Chen Jinyun Liu Xudong Li Jie Sun Dianhua Zhang |
| author_sort | Yulong Hu |
| collection | DOAJ |
| description | Abstract The roller is a crucial tool in the hot-rolled strip steel manufacturing process. Rapid and accurate detection of roll surface defects is essential for enhancing product surface quality, ensuring dimensional precision, and reducing scrap rates. Currently, industrial roll inspection primarily relies on manual visual assessment, which is prone to subjectivity, low accuracy, and inefficiency. To overcome these limitations, this study introduces a defect detection method based on transfer learning and an enhanced YOLOv5 model.Given the lack of publicly available datasets for roll surface defects, a representative dataset was first constructed using defect images collected from a steel production line. To further optimize the detection model, the NEU-DET dataset—containing strip surface defect images—was employed to pre-train the improved YOLOv5 model, refining its parameters. The pre-trained model was then adapted for roll defect detection using transfer learning, where the learned parameters from strip surface defects served as initial weights for training on the roll defect dataset. Experimental results demonstrate that the proposed TR-CNF-YOLOv5 model, integrating transfer learning with an improved YOLOv5 architecture, outperforms existing models. Specifically, it achieves an mAP improvement of approximately 9.8% over the original YOLOv5 and 7.1% over YOLOv7 on the roll surface defect dataset. |
| format | Article |
| id | doaj-art-0e963bf6efea467f9936215c5c351d97 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-0e963bf6efea467f9936215c5c351d972025-08-20T03:47:16ZengSpringerDiscover Applied Sciences3004-92612025-06-017711410.1007/s42452-025-06920-7Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5Yulong Hu0Wen Peng1Shiyi Chen2Jinyun Liu3Xudong Li4Jie Sun5Dianhua Zhang6State Key Laboratory of Digital Steel, Northeastern UniversityState Key Laboratory of Digital Steel, Northeastern UniversityZhongxing Telecom EquipmentCollege of Artificial Intelligence, North China University of Science and TechnologyResearch Institute of Technology Shougang Group Co. LtdState Key Laboratory of Digital Steel, Northeastern UniversityState Key Laboratory of Digital Steel, Northeastern UniversityAbstract The roller is a crucial tool in the hot-rolled strip steel manufacturing process. Rapid and accurate detection of roll surface defects is essential for enhancing product surface quality, ensuring dimensional precision, and reducing scrap rates. Currently, industrial roll inspection primarily relies on manual visual assessment, which is prone to subjectivity, low accuracy, and inefficiency. To overcome these limitations, this study introduces a defect detection method based on transfer learning and an enhanced YOLOv5 model.Given the lack of publicly available datasets for roll surface defects, a representative dataset was first constructed using defect images collected from a steel production line. To further optimize the detection model, the NEU-DET dataset—containing strip surface defect images—was employed to pre-train the improved YOLOv5 model, refining its parameters. The pre-trained model was then adapted for roll defect detection using transfer learning, where the learned parameters from strip surface defects served as initial weights for training on the roll defect dataset. Experimental results demonstrate that the proposed TR-CNF-YOLOv5 model, integrating transfer learning with an improved YOLOv5 architecture, outperforms existing models. Specifically, it achieves an mAP improvement of approximately 9.8% over the original YOLOv5 and 7.1% over YOLOv7 on the roll surface defect dataset.https://doi.org/10.1007/s42452-025-06920-7Steel industryObject detectionTransfer learningImage classification |
| spellingShingle | Yulong Hu Wen Peng Shiyi Chen Jinyun Liu Xudong Li Jie Sun Dianhua Zhang Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5 Discover Applied Sciences Steel industry Object detection Transfer learning Image classification |
| title | Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5 |
| title_full | Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5 |
| title_fullStr | Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5 |
| title_full_unstemmed | Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5 |
| title_short | Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5 |
| title_sort | surface defect detection on rolling mill rolls a transfer learning approach with improved yolov5 |
| topic | Steel industry Object detection Transfer learning Image classification |
| url | https://doi.org/10.1007/s42452-025-06920-7 |
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