JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (9): 62-70.doi: 10.6040/j.issn.1671-9352.0.2024.077

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Improvement of the YOLOv7-tiny visual-assisted lightweight algorithm based on high-channel convolution

OUYANG Yuxuan, PENG Yaopan, ZHANG Rongfen*, LIU Yuhong   

  1. College of Big Data and Information Engineering, Guizhou University, Guiyang 50025, Guizhou, China
  • Published:2025-09-10

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Abstract: In response to the issues of large parameter size, low detection performance, and inconvenience for deployment on mobile devices in most existing visual assistance system algorithms, a lightweight visual assistance algorithm is designed based on YOLOv7-tiny. The receptive field block(RFB)is used in the network to fuse the feature information of different scales to improve the detection accuracy of objects of different resolution sizes. The non-linearity of activation function sigmoid linear unit(Silu)is used to enhance the fitting ability of the model, improve the learning speed and detection accuracy of the model. Finally, the depth-wise convolution(DWConv)with better performance is selected by comparison experiment to achieve the lightweight of the model. The experimental results show that the parameters of the improved lightweight model are reduced by 52.1% compared with the original model, and the best detection performance is obtained. Compared with other mainstream object detection algorithms, this algorithm achieves more accurate real-time detection of indoor objects with 2.90 M parameters.

Key words: high-channel convolution, receptive field block, activation function Silu, DWConv

CLC Number: 

  • TP391.41
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