Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5 Article Swipe
Yulong Hu
,
Peng Wen
,
Shiyi Chen
,
Jinyun Liu
,
Xudong Li
,
Jie Sun
,
Dianhua Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s42452-025-06920-7
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s42452-025-06920-7
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s42452-025-06920-7
- https://link.springer.com/content/pdf/10.1007/s42452-025-06920-7.pdf
- OA Status
- diamond
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411409987
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4411409987Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s42452-025-06920-7Digital Object Identifier
- Title
-
Surface defect detection on rolling mill rolls: a transfer learning approach with improved YOLOv5Work title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-06-18Full publication date if available
- Authors
-
Yulong Hu, Peng Wen, Shiyi Chen, Jinyun Liu, Xudong Li, Jie Sun, Dianhua ZhangList of authors in order
- Landing page
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https://doi.org/10.1007/s42452-025-06920-7Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s42452-025-06920-7.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s42452-025-06920-7.pdfDirect OA link when available
- Concepts
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Transfer of learning, Computer science, Surface (topology), Scrap, Process (computing), Artificial intelligence, Production line, Engineering, Mechanical engineering, Mathematics, Geometry, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| primary_location.source.display_name | Discover Applied Sciences |
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| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s42452-025-06920-7.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
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| primary_location.landing_page_url | https://doi.org/10.1007/s42452-025-06920-7 |
| publication_date | 2025-06-18 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3168685493, https://openalex.org/W4296871629, https://openalex.org/W3206415899, https://openalex.org/W3212471930, https://openalex.org/W3132342156, https://openalex.org/W3015486657, https://openalex.org/W4403812047, https://openalex.org/W4376141703, https://openalex.org/W3013050809, https://openalex.org/W3210062351, https://openalex.org/W3203368756, https://openalex.org/W3162138249, https://openalex.org/W3126618193, https://openalex.org/W4398202167, https://openalex.org/W4317796687, https://openalex.org/W3163646131, https://openalex.org/W4225321635, https://openalex.org/W4393376891, https://openalex.org/W4225616511, https://openalex.org/W2944303778, https://openalex.org/W4382059335, https://openalex.org/W2928165649, https://openalex.org/W3215916782, https://openalex.org/W3194790201, https://openalex.org/W2990763144, https://openalex.org/W3122173535, https://openalex.org/W1986614398, https://openalex.org/W4379647709, https://openalex.org/W4402130399, https://openalex.org/W3081792418, https://openalex.org/W4399090816 |
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| abstract_inverted_index.and | 15, 32, 53, 70, 187 |
| abstract_inverted_index.for | 24, 81, 129, 148 |
| abstract_inverted_index.its | 121 |
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| abstract_inverted_index.images | 93 |
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| abstract_inverted_index.improved | 117, 168 |
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| abstract_inverted_index.optimize | 102 |
| abstract_inverted_index.original | 185 |
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| abstract_inverted_index.essential | 23 |
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| abstract_inverted_index.TR-CNF-YOLOv5 | 161 |
| abstract_inverted_index.approximately | 181 |
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| abstract_inverted_index.manufacturing | 12 |
| abstract_inverted_index.subjectivity, | 50 |
| abstract_inverted_index.representative | 86 |
| abstract_inverted_index.dataset—containing | 108 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.31246109 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |