Improved YOLOv5-Based Electric Bicycle Detection Algorithm in Elevators Using State Space Models.
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| Title: | Improved YOLOv5-Based Electric Bicycle Detection Algorithm in Elevators Using State Space Models. |
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| Authors: | Lingzhi Wang, Yingfan Wu |
| Source: | Engineering Letters. Nov2025, Vol. 33 Issue 11, p4584-4592. 9p. |
| Subjects: | Electric bicycles, Elevators, Real-time computing, Object recognition (Computer vision), Feature extraction, State-space methods, Edge computing, Fire risk assessment |
| Abstract: | As electric bicycle use grows, indoor chargingrelated fires have surged, posing serious risks to life and property. Therefore, a real-time and accurate method for detecting electric bicycles in elevators is of great research significance. Addressing complex equipment issues and elevated false positive rates in existing detection methods, this paper proposes an electric bicycle detection algorithm for elevators based on an improved YOLOv5 using a State Space Model (SSM). First, lightweight GhostConv replaces the standard convolution to reduce the number of parameters. Second, the SSM-Conv module is introduced to replace the C3 structure, leveraging the context modeling of the Mamba block to enhance feature extraction capabilities. Finally, a 2D selective scanbased SS2D-Fusion module is designed for feature fusion and integrated into the Neck part, aiming at improving feature fusion capabilities through cross-layer information interaction and enhanced context modeling. Additionally, we incorporate a sample-difficulty-aware SlideLoss into the training framework to dynamically balance the loss contributions between hard and regular samples. Experimental results demonstrate that the improved YOLOv5n model achieves a 1.6% increase in mAP0.5 and a 6.9% increase in mAP0.5:0.95 compared to the original model, while reducing the number of parameters by 14.7%. Consequently, the proposed model enhances detection accuracy and reduces the parameter count, making it well-suited for edge computing devices. It effectively improves both the accuracy and real-time performance of electric bicycle detection in elevators. [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: 189071738 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Improved YOLOv5-Based Electric Bicycle Detection Algorithm in Elevators Using State Space Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lingzhi+Wang%22">Lingzhi Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Yingfan+Wu%22">Yingfan Wu</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Nov2025, Vol. 33 Issue 11, p4584-4592. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electric+bicycles%22">Electric bicycles</searchLink><br /><searchLink fieldCode="DE" term="%22Elevators%22">Elevators</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22State-space+methods%22">State-space methods</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Fire+risk+assessment%22">Fire risk assessment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As electric bicycle use grows, indoor chargingrelated fires have surged, posing serious risks to life and property. Therefore, a real-time and accurate method for detecting electric bicycles in elevators is of great research significance. Addressing complex equipment issues and elevated false positive rates in existing detection methods, this paper proposes an electric bicycle detection algorithm for elevators based on an improved YOLOv5 using a State Space Model (SSM). First, lightweight GhostConv replaces the standard convolution to reduce the number of parameters. Second, the SSM-Conv module is introduced to replace the C3 structure, leveraging the context modeling of the Mamba block to enhance feature extraction capabilities. Finally, a 2D selective scanbased SS2D-Fusion module is designed for feature fusion and integrated into the Neck part, aiming at improving feature fusion capabilities through cross-layer information interaction and enhanced context modeling. Additionally, we incorporate a sample-difficulty-aware SlideLoss into the training framework to dynamically balance the loss contributions between hard and regular samples. Experimental results demonstrate that the improved YOLOv5n model achieves a 1.6% increase in mAP0.5 and a 6.9% increase in mAP0.5:0.95 compared to the original model, while reducing the number of parameters by 14.7%. Consequently, the proposed model enhances detection accuracy and reduces the parameter count, making it well-suited for edge computing devices. It effectively improves both the accuracy and real-time performance of electric bicycle detection in elevators. [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: 9 StartPage: 4584 Subjects: – SubjectFull: Electric bicycles Type: general – SubjectFull: Elevators Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: State-space methods Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Fire risk assessment Type: general Titles: – TitleFull: Improved YOLOv5-Based Electric Bicycle Detection Algorithm in Elevators Using State Space Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lingzhi Wang – PersonEntity: Name: NameFull: Yingfan Wu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 33 – Type: issue Value: 11 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
