An improved multi-scale fusion YOLOv7-tiny algorithm based on Ghost efficient layer aggregation network.

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Bibliographic Details
Title:	An improved multi-scale fusion YOLOv7-tiny algorithm based on Ghost efficient layer aggregation network.
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]
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Database:	Engineering Source

Description
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]
ISSN:	1007130X
DOI:	10.3969/j.issn.1007-130X.2025.08.011