A fine-grained image classification method combining YOLOv7 and bilinear multi-level feature fusion
Authors: 
Min Huang, Zehua Wang, Saixing ZhuAuthors Info & Claims
Min Huang, Zehua Wang, Saixing ZhuAuthors Info & ClaimsPages 1 - 5
Abstract
Fine grain image is an important research field of image recognition, which can classify objects in more detail. Image feature extraction is usually implemented by bilinear network, which can meet the desired effect of feature extraction, but also discard some detailed feature information. In this study, a fine-grained classification optimization algorithm that combines the target detection method YOLOv7 and multi-feature fusion bilinear network is proposed to achieve fine-grained image classification. In the method, YOLOv7 is used to locate the target, and the recognition content is extracted according to the image detection frame; Then, the improved bilinear convolutional neural network structure is used to fuse the features of different channels and different levels of convolutional layers in the bilinear network, so as to obtain more feature information and improve the precision of fine-grained classification. Experimental results show that the classification results of this algorithm are better.
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Index Terms
- A fine-grained image classification method combining YOLOv7 and bilinear multi-level feature fusion
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Published In
December 2022
365 pages
ISBN:9781450398039
DOI:10.1145/3579895
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 April 2023
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- Research-article
- Research
- Refereed limited
Funding Sources
- Henan Province's key research and development and promotion project (scientific and technological breakthrough), multi-channel magnetic resonance phase reconstruction method research
Conference
ICNCC 2022
ICNCC 2022: 2022 The 11th International Conference on Networks, Communication and Computing
December 9 - 11, 2022
Beijing, China
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