A Two-Stage Automatic Framework For Rail Surface Inspection Using YOLOv10 And AutoML.
Saved in:
| Title: | A Two-Stage Automatic Framework For Rail Surface Inspection Using YOLOv10 And AutoML. |
|---|---|
| Authors: | Ha, Minh Tuan1 minhtuanha031@gmail.com, Nguyen, Phuong Ty2 tynp2109@gmail.com |
| Source: | IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2764-2777. 14p. |
| Subjects: | Inspection & review, Object recognition (Computer vision), Neural architecture search, Railroad safety measures, Real-time computing, Convolutional neural networks |
| Abstract: | Automated visual inspection of rail surfaces is critical for railway safety but faces challenges such as complex backgrounds, diverse defects, and environmental noise. This paper proposes a fully automated, two-stage framework integrating YOLOv10 and Automated Machine Learning (AutoML). First, YOLOv10 detects and extracts the rail region to eliminate background interference. Second, an AutoML pipeline automatically designs an optimal hybrid convolutional neural network (CNN) for defect classification, combining components from VGG16, MobileNet, and EfficientNetV2. Experimental results demonstrate the framework's high performance, achieving a 99.3% classification accuracy on an independent test set while maintaining a processing speed suitable for real-time applications. This work highlights the efficacy of combining targeted region extraction with AutoML for robust and efficient rail surface defect inspection. [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 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 195088904 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A Two-Stage Automatic Framework For Rail Surface Inspection Using YOLOv10 And AutoML. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ha%2C+Minh+Tuan%22">Ha, Minh Tuan</searchLink><relatesTo>1</relatesTo><i> minhtuanha031@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Phuong+Ty%22">Nguyen, Phuong Ty</searchLink><relatesTo>2</relatesTo><i> tynp2109@gmail.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, p2764-2777. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Inspection+%26+review%22">Inspection & review</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+architecture+search%22">Neural architecture search</searchLink><br /><searchLink fieldCode="DE" term="%22Railroad+safety+measures%22">Railroad safety measures</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Automated visual inspection of rail surfaces is critical for railway safety but faces challenges such as complex backgrounds, diverse defects, and environmental noise. This paper proposes a fully automated, two-stage framework integrating YOLOv10 and Automated Machine Learning (AutoML). First, YOLOv10 detects and extracts the rail region to eliminate background interference. Second, an AutoML pipeline automatically designs an optimal hybrid convolutional neural network (CNN) for defect classification, combining components from VGG16, MobileNet, and EfficientNetV2. Experimental results demonstrate the framework's high performance, achieving a 99.3% classification accuracy on an independent test set while maintaining a processing speed suitable for real-time applications. This work highlights the efficacy of combining targeted region extraction with AutoML for robust and efficient rail surface defect inspection. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195088904 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 2764 Subjects: – SubjectFull: Inspection & review Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Neural architecture search Type: general – SubjectFull: Railroad safety measures Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Convolutional neural networks Type: general Titles: – TitleFull: A Two-Stage Automatic Framework For Rail Surface Inspection Using YOLOv10 And AutoML. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ha, Minh Tuan – PersonEntity: Name: NameFull: Nguyen, Phuong Ty 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 |
