ARL-YOLO: A Lightweight Steel Surface Defect Detection Algorithm.
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| Title: | ARL-YOLO: A Lightweight Steel Surface Defect Detection Algorithm. |
|---|---|
| Authors: | Sun, Xinguo1 410525197@qq.com, Tao, Ye2 taibeijack@163.com, Cui, Wenhua3 taibeijack@126.com, Liu, Huiyan4 micmoo@126.com, Yu, Yang5 1364744489@qq.com, Sun, Xinli6 501980329@qq.com |
| Source: | IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2629-2639. 11p. |
| Subjects: | Algorithms, Object recognition (Computer vision), Edge computing |
| Abstract: | Steel surface defect detection is the key link of quality control in metallurgical industry, which directly affects product performance and safety production. Steel surface defects are diverse, complex in structure, and belong to the type of small targets. Mainstream detection algorithms suffer from excessive parameters and high computational complexity, hindering their deployment on resource-constrained embedded devices. To address this issue, this paper proposes the ARL-YOLO algorithm based on YOLOv8n, incorporating three key lightweight strategies: (1) replacing the SPPF module with the intra-scale feature interaction module AIFI to enhance small-target feature capture; (2) introducing the RGCSPELAN lightweight module, whose multi-scale fusion mechanism improves perception of objects of varying sizes in complex scenes; (3) designing a lightweight shared convolutional detection head (LSCD) to reduce computational load and parameters while maintaining accuracy. Experimental results show that compared with YOLOv8n, ARL-YOLO achieves a 1.0% accuracy improvement, with model size reduced to 46.9% of the original and parameters down to 1.48MB. This lightweight model lowers deployment costs and is well-suited for edge server applications. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195088892 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ARL-YOLO: A Lightweight Steel Surface Defect Detection Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Xinguo%22">Sun, Xinguo</searchLink><relatesTo>1</relatesTo><i> 410525197@qq.com</i><br /><searchLink fieldCode="AR" term="%22Tao%2C+Ye%22">Tao, Ye</searchLink><relatesTo>2</relatesTo><i> taibeijack@163.com</i><br /><searchLink fieldCode="AR" term="%22Cui%2C+Wenhua%22">Cui, Wenhua</searchLink><relatesTo>3</relatesTo><i> taibeijack@126.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Huiyan%22">Liu, Huiyan</searchLink><relatesTo>4</relatesTo><i> micmoo@126.com</i><br /><searchLink fieldCode="AR" term="%22Yu%2C+Yang%22">Yu, Yang</searchLink><relatesTo>5</relatesTo><i> 1364744489@qq.com</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Xinli%22">Sun, Xinli</searchLink><relatesTo>6</relatesTo><i> 501980329@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jul2026, Vol. 53 Issue 7, p2629-2639. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Steel surface defect detection is the key link of quality control in metallurgical industry, which directly affects product performance and safety production. Steel surface defects are diverse, complex in structure, and belong to the type of small targets. Mainstream detection algorithms suffer from excessive parameters and high computational complexity, hindering their deployment on resource-constrained embedded devices. To address this issue, this paper proposes the ARL-YOLO algorithm based on YOLOv8n, incorporating three key lightweight strategies: (1) replacing the SPPF module with the intra-scale feature interaction module AIFI to enhance small-target feature capture; (2) introducing the RGCSPELAN lightweight module, whose multi-scale fusion mechanism improves perception of objects of varying sizes in complex scenes; (3) designing a lightweight shared convolutional detection head (LSCD) to reduce computational load and parameters while maintaining accuracy. Experimental results show that compared with YOLOv8n, ARL-YOLO achieves a 1.0% accuracy improvement, with model size reduced to 46.9% of the original and parameters down to 1.48MB. This lightweight model lowers deployment costs and is well-suited for edge server applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 2629 Subjects: – SubjectFull: Algorithms Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Edge computing Type: general Titles: – TitleFull: ARL-YOLO: A Lightweight Steel Surface Defect Detection Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Xinguo – PersonEntity: Name: NameFull: Tao, Ye – PersonEntity: Name: NameFull: Cui, Wenhua – PersonEntity: Name: NameFull: Liu, Huiyan – PersonEntity: Name: NameFull: Yu, Yang – PersonEntity: Name: NameFull: Sun, Xinli IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
| ResultId | 1 |
