An improved multi-scale fusion YOLOv7-tiny algorithm based on Ghost efficient layer aggregation network.
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| Title: | An improved multi-scale fusion YOLOv7-tiny algorithm based on Ghost efficient layer aggregation network. |
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| Authors: | OUYANG, Yuxuan1 1621413779@qq.com, ZHANG, Rongfen1 rfzhang@gzu.edu.cn, LIU, Yuhong1 1693623574@qq.com, PENG, Yaopan1 1499535403@qq.com |
| Source: | Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Aug2025, Vol. 47 Issue 8, p1437-1448. 12p. |
| Subjects: | Models & modelmaking, Information networks, Detection limit, Learning modules, Algorithms |
| Abstract: | To address the common issues of excessive parameters, slow inference speed, limited detection performance, and difficulty in deploying neural networks on edge devices, this paper proposes an improved YOLOv7-tiny algorithm. Firstly, according to the characteristics of the original algorithm model structure, Ghost-ELAN module is introduced to compress the model greatly. Secondly, Ghost Bottleneck-2 is used to replace the convolution of the Neck part of the network, which further reduces the scale of the model. Then, the multi-scale fusion module Ghost-SPPCSPC is used to improve the understanding of feature information of the model, and the output layer convolution is replaced by GhostConv, which reduces the redundancy of common convolution and makes the maximum use of semantic information in the network. Finally, transfer learning is employed for enhancing generalized feature learning and improving performance of the model. Experimental results demonstrate that the improved model reduces parameter count and model size by 57.19% and 55.28%, respectively, achieving substantial compression over the original model while enhancing accuracy. With an inference speed of 278, the proposed model attains rapid, efficient, and lightweight objectives, making it highly suitable for deployment on edge devices. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science 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: 188160260 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An improved multi-scale fusion YOLOv7-tiny algorithm based on Ghost efficient layer aggregation network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22OUYANG%2C+Yuxuan%22">OUYANG, Yuxuan</searchLink><relatesTo>1</relatesTo><i> 1621413779@qq.com</i><br /><searchLink fieldCode="AR" term="%22ZHANG%2C+Rongfen%22">ZHANG, Rongfen</searchLink><relatesTo>1</relatesTo><i> rfzhang@gzu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22LIU%2C+Yuhong%22">LIU, Yuhong</searchLink><relatesTo>1</relatesTo><i> 1693623574@qq.com</i><br /><searchLink fieldCode="AR" term="%22PENG%2C+Yaopan%22">PENG, Yaopan</searchLink><relatesTo>1</relatesTo><i> 1499535403@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Engineering+%26+Science+%2F+Jisuanji+Gongcheng+yu+Kexue%22">Computer Engineering & Science / Jisuanji Gongcheng yu Kexue</searchLink>. Aug2025, Vol. 47 Issue 8, p1437-1448. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Models+%26+modelmaking%22">Models & modelmaking</searchLink><br /><searchLink fieldCode="DE" term="%22Information+networks%22">Information networks</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+limit%22">Detection limit</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+modules%22">Learning modules</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To address the common issues of excessive parameters, slow inference speed, limited detection performance, and difficulty in deploying neural networks on edge devices, this paper proposes an improved YOLOv7-tiny algorithm. Firstly, according to the characteristics of the original algorithm model structure, Ghost-ELAN module is introduced to compress the model greatly. Secondly, Ghost Bottleneck-2 is used to replace the convolution of the Neck part of the network, which further reduces the scale of the model. Then, the multi-scale fusion module Ghost-SPPCSPC is used to improve the understanding of feature information of the model, and the output layer convolution is replaced by GhostConv, which reduces the redundancy of common convolution and makes the maximum use of semantic information in the network. Finally, transfer learning is employed for enhancing generalized feature learning and improving performance of the model. Experimental results demonstrate that the improved model reduces parameter count and model size by 57.19% and 55.28%, respectively, achieving substantial compression over the original model while enhancing accuracy. With an inference speed of 278, the proposed model attains rapid, efficient, and lightweight objectives, making it highly suitable for deployment on edge devices. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science 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: Identifiers: – Type: doi Value: 10.3969/j.issn.1007-130X.2025.08.011 Languages: – Code: chi Text: Chinese PhysicalDescription: Pagination: PageCount: 12 StartPage: 1437 Subjects: – SubjectFull: Models & modelmaking Type: general – SubjectFull: Information networks Type: general – SubjectFull: Detection limit Type: general – SubjectFull: Learning modules Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: An improved multi-scale fusion YOLOv7-tiny algorithm based on Ghost efficient layer aggregation network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: OUYANG, Yuxuan – PersonEntity: Name: NameFull: ZHANG, Rongfen – PersonEntity: Name: NameFull: LIU, Yuhong – PersonEntity: Name: NameFull: PENG, Yaopan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1007130X Numbering: – Type: volume Value: 47 – Type: issue Value: 8 Titles: – TitleFull: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue Type: main |
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