MFSF-YOLO: An Improved YOLO11 Model for Traffic Sign Detection and Recognition.
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| Title: | MFSF-YOLO: An Improved YOLO11 Model for Traffic Sign Detection and Recognition. |
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| Authors: | Ma, Simin1 1820077692@qq.com, Zhao, Nannan2 723306003@qq.com, Li, Jiangwei1 2369156940@qq.com, Ouyang, Xinyu1 13392862@qq.com |
| Source: | Engineering Letters. Jun2026, Vol. 34 Issue 6, p2225-2235. 11p. |
| Subjects: | Feature extraction, Object recognition (Computer vision), Cost functions, Computer vision, Traffic signs & signals, Signal detection |
| Abstract: | Aiming at the problems of high false detection rate, high missed detection rate, low precision, and poor robustness in traffic target detection within computer vision tasks, a model called MFSF-YOLO11 (YOLO11 with Multi-directional information Flow and Scale-adaptive Fusion) is proposed. First, an improved feature extraction method is proposed. By designing the SPPCAKO module to replace the original SPPF module, the capability and accuracy of feature extraction are effectively enhanced. In terms of feature fusion, an innovative SDI-Damo Neck structure is put forward, which significantly improves the detection accuracy and robustness of YOLO series models in practical applications. To further boost the detection performance, a brand-new OASFFHead structure is introduced in the detection head part. This structure can simultaneously take into account the directionality and scale variation of targets, thereby optimizing the overall performance of target detection. Finally, the InnerMPDIoU loss function is adopted to replace the traditional CIoU loss function, which not only improves the accuracy of target localization but also remarkably optimizes the fitting effect of detection boxes. Experimental results show that the improved YOLO11 model achieves a 4.8% increase in mAP50, an 8.2% improvement in precision, and a 15.8% rise in recall on the TT100K dataset, demonstrating higher accuracy and robustness in complex environments. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters 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: 194195703 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: MFSF-YOLO: An Improved YOLO11 Model for Traffic Sign Detection and Recognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ma%2C+Simin%22">Ma, Simin</searchLink><relatesTo>1</relatesTo><i> 1820077692@qq.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Nannan%22">Zhao, Nannan</searchLink><relatesTo>2</relatesTo><i> 723306003@qq.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Jiangwei%22">Li, Jiangwei</searchLink><relatesTo>1</relatesTo><i> 2369156940@qq.com</i><br /><searchLink fieldCode="AR" term="%22Ouyang%2C+Xinyu%22">Ouyang, Xinyu</searchLink><relatesTo>1</relatesTo><i> 13392862@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jun2026, Vol. 34 Issue 6, p2225-2235. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+signs+%26+signals%22">Traffic signs & signals</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Aiming at the problems of high false detection rate, high missed detection rate, low precision, and poor robustness in traffic target detection within computer vision tasks, a model called MFSF-YOLO11 (YOLO11 with Multi-directional information Flow and Scale-adaptive Fusion) is proposed. First, an improved feature extraction method is proposed. By designing the SPPCAKO module to replace the original SPPF module, the capability and accuracy of feature extraction are effectively enhanced. In terms of feature fusion, an innovative SDI-Damo Neck structure is put forward, which significantly improves the detection accuracy and robustness of YOLO series models in practical applications. To further boost the detection performance, a brand-new OASFFHead structure is introduced in the detection head part. This structure can simultaneously take into account the directionality and scale variation of targets, thereby optimizing the overall performance of target detection. Finally, the InnerMPDIoU loss function is adopted to replace the traditional CIoU loss function, which not only improves the accuracy of target localization but also remarkably optimizes the fitting effect of detection boxes. Experimental results show that the improved YOLO11 model achieves a 4.8% increase in mAP50, an 8.2% improvement in precision, and a 15.8% rise in recall on the TT100K dataset, demonstrating higher accuracy and robustness in complex environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters 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: 2225 Subjects: – SubjectFull: Feature extraction Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Cost functions Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Traffic signs & signals Type: general – SubjectFull: Signal detection Type: general Titles: – TitleFull: MFSF-YOLO: An Improved YOLO11 Model for Traffic Sign Detection and Recognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ma, Simin – PersonEntity: Name: NameFull: Zhao, Nannan – PersonEntity: Name: NameFull: Li, Jiangwei – PersonEntity: Name: NameFull: Ouyang, Xinyu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 6 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
