EFFE-YOLO: An Improved Algorithm for Small-Target Traffic Sign Detection.
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| Title: | EFFE-YOLO: An Improved Algorithm for Small-Target Traffic Sign Detection. |
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| Authors: | Lu, Guangyao1 18838927982@163.com, Liu, Weisheng2 succman@163.com |
| Source: | Engineering Letters. Jun2026, Vol. 34 Issue 6, p2336-2349. 14p. |
| Subjects: | Traffic signs & signals, Object recognition (Computer vision), Computer vision |
| Abstract: | Extreme scale variation and severe occlusion in complex driving environments significantly degrade the detection performance of small traffic signs. To address these challenges, this paper presents EFFE-YOLO, an accurate detection model based on YOLOv11n. Specifically, the Cross-Stage Partial Network and Parallel Multi-Scale Feature Fusion Attention (CSP-PMSFA) module enhances deep multi-scale semantic feature extraction, and the Cross-scale Alignment Zone Neck (CAZ Neck) achieves seamless cross-scale feature alignment. The Adaptive Downsampling (ADown) algorithm reduces information loss and preserves fine-grained details critical for small targets. Furthermore, the integration of Scale Sequence Feature Fusion (ScalSeq) and the P2 detection layer effectively fuses multi-scale feature maps, retains high-resolution information for small targets, and strengthens the model's multi-scale detection ability. We propose an Inner-GIoU loss function equipped with auxiliary bounding boxes and a scaling factor mechanism to improve bounding box regression accuracy. Experimental results show that EFFE-YOLO achieves 77.99% mAP@50 and 60.24% mAP@50:95 on TT100K, with improvements of 1.82% mAP@50 and 3.07% mAP@50:95 on CCTSDB over the baseline model. Specifically, it yields a 24.98% mAP@50 improvement for small targets on TT100K, validating its superiority in small-scale traffic sign detection. [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: 194195714 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: EFFE-YOLO: An Improved Algorithm for Small-Target Traffic Sign Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lu%2C+Guangyao%22">Lu, Guangyao</searchLink><relatesTo>1</relatesTo><i> 18838927982@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Weisheng%22">Liu, Weisheng</searchLink><relatesTo>2</relatesTo><i> succman@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jun2026, Vol. 34 Issue 6, p2336-2349. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Traffic+signs+%26+signals%22">Traffic signs & signals</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Extreme scale variation and severe occlusion in complex driving environments significantly degrade the detection performance of small traffic signs. To address these challenges, this paper presents EFFE-YOLO, an accurate detection model based on YOLOv11n. Specifically, the Cross-Stage Partial Network and Parallel Multi-Scale Feature Fusion Attention (CSP-PMSFA) module enhances deep multi-scale semantic feature extraction, and the Cross-scale Alignment Zone Neck (CAZ Neck) achieves seamless cross-scale feature alignment. The Adaptive Downsampling (ADown) algorithm reduces information loss and preserves fine-grained details critical for small targets. Furthermore, the integration of Scale Sequence Feature Fusion (ScalSeq) and the P2 detection layer effectively fuses multi-scale feature maps, retains high-resolution information for small targets, and strengthens the model's multi-scale detection ability. We propose an Inner-GIoU loss function equipped with auxiliary bounding boxes and a scaling factor mechanism to improve bounding box regression accuracy. Experimental results show that EFFE-YOLO achieves 77.99% mAP@50 and 60.24% mAP@50:95 on TT100K, with improvements of 1.82% mAP@50 and 3.07% mAP@50:95 on CCTSDB over the baseline model. Specifically, it yields a 24.98% mAP@50 improvement for small targets on TT100K, validating its superiority in small-scale traffic sign detection. [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: 14 StartPage: 2336 Subjects: – SubjectFull: Traffic signs & signals Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Computer vision Type: general Titles: – TitleFull: EFFE-YOLO: An Improved Algorithm for Small-Target Traffic Sign Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lu, Guangyao – PersonEntity: Name: NameFull: Liu, Weisheng 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 |
