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.
References
[1]
Kiapour M, Wei D, Jagadeesh V, FINE-GRAINED CATEGORIZATION:, US20180039866A1[P]. 2018
[2]
Luo J H, Wu J X. A survey on fine-grained image categorization using deep convolutional features[J]. Acta Automatica Sinica, 2017, 43(8): 1306-1318
[3]
Lin T Y. RoyChowdhury A, Maji S. Bilinear convolutional neural networks for fine-grained visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(6): 1309-1322.
[4]
Gormus, Tunc E . Improved classification of remote sensing imagery using image fusion techniques[C]// IEEE International Conference on Computational Intelligence & Cybernetics. IEEE, 2013.
[5]
Qin X, Song G F. Pig face recognition algorithm based on bilinear convolution neural network[J]. Journal of Hangzhou Dianzi University, 2019, 39(2).
[6]
Yan Z X,Hou Z Q,Xiong L,Liu X Y,Yu W S and Ma S G.2021.Fine-grained classification based on bilinear feature fusion and YOLOv3.Journal of Image and Graphics,26(04):0847-0856,847-856.
[7]
Zhu X, Bain M. B-CNN: branch convolutional neural network for hierarchical classification[J]. arXiv preprint arXiv:1709.09890, 2017.
[8]
Wang C Y, Bochkovskiy A, Liao H . YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J].
[9]
Kumar C, Punitha R. Yolov3 and yolov4: Multiple object detection for surveillance applications[C]//2020 Third international conference on smart systems and inventive technology (ICSSIT). IEEE, 2020: 1316-1321.
[10]
Qinan Li, Haixin Sun, Kejia Sun. Fine-grained Classification of Sleeper Shoulder Crack Images Based on Improved B-CNN[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141013
[11]
Wah C, Branson S, Welinder P, The caltech-ucsd birds-200-2011 dataset[J]. 2011.
[12]
Khosla A, Jayadevaprakash N, Yao B, Novel dataset for fine-grained image categorization: Stanford dogs[C]//Proc. CVPR workshop on fine-grained visual categorization (FGVC). Citeseer, 2011, 2(1).
[13]
Yu Y, Hao K, Ding Y. A new image classification model based on brain parallel interaction mechanism[J]. Neurocomputing, 2018, 315: 190-197.
[14]
Zhao B, Wu X, Feng J, Diversified visual attention networks for fine-grained object classification[J]. IEEE Transactions on Multimedia, 2017, 19(6):DVAN(diversified visual attention networks) DVAN(diversified visual attention networks)
[15]
Shi Weiwei,Gong Yihong,Tao Xiaoyu,Cheng De,Zheng Nanning. Fine-Grained Image Classification Using Modified DCNNs Trained by Cascaded Softmax and Generalized Large-Margin Losses.[J]. IEEE transactions on neural networks and learning systems,2019,30(3).
[16]
Chang J R, Chen Y S . Exploiting Spatial Relation for Reducing Distortion in Style Transfer[C]// Workshop on Applications of Computer Vision. Institute of Electrical and Electronics Engineers Inc.
[17]
Gormus, Tunc E . Improved classification of remote sensing imagery using image fusion techniques[C]// IEEE International Conference on Computational Intelligence & Cybernetics. IEEE, 2013.
[18]
Wu H, Li Y, Zhou L, Convolutional neural network and multi‐feature fusion for automatic modulation classification[J]. Electronics Letters, 2019, 55(16): 895-897.
[19]
Wei L, Wang K, Lu Q, Crops fine classification in airborne hyperspectral imagery based on multi-feature fusion and deep learning[J]. Remote Sensing, 2021, 13(15): 2917.
[20]
Wang, Q., Xie, J., Zuo, W., Zhang, L., & Li, P. . (2019). Deep cnns meet global covariance pooling: better representation and generalization. arXiv e-prints.
[21]
Wang Y X, Zhang X B. Fine-grained image classification with network architecture of focus and recognition[J]. Journal of Image and Graphics, 2019, 24(4): 493-502.
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
Copyright © 2022 ACM.
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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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