Flower image classification based on an improved lightweight neural network with multi-scale feature fusion and attention mechanism
-
Published:2023
Issue:8
Volume:20
Page:13900-13920
-
ISSN:1551-0018
-
Container-title:Mathematical Biosciences and Engineering
-
language:
-
Short-container-title:MBE
Author:
Zeng Zhigao12, Huang Cheng12, Zhu Wenqiu12, Wen Zhiqiang12, Yuan Xinpan12
Affiliation:
1. School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan 412007, China 2. Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Zhuzhou, Hunan 412007, China
Abstract
<abstract><p>In order to solve the problem that deep learning-based flower image classification methods lose more feature information in the early feature extraction process, and the model takes up more storage space, a new lightweight neural network model based on multi-scale feature fusion and attention mechanism is proposed in this paper. First, the AlexNet model is chosen as the basic framework. Second, a multi-scale feature fusion module (MFFM) is used to replace the shallow single-scale convolution. MFFM, which contains three depthwise separable convolution branches with different sizes, can fuse features with different scales and reduce the feature loss caused by single-scale convolution. Third, two layers of improved Inception module are first added to enhance the extraction of deep features, and a layer of hybrid attention module is added to strengthen the focus of the model on key information at a later stage. Finally, the flower image classification is completed using a combination of global average pooling and fully connected layers. The experimental results demonstrate that our lightweight model has fewer parameters, takes up less storage space and has higher classification accuracy than the baseline model, which helps to achieve more accurate flower image recognition on mobile devices.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference34 articles.
1. H. Hiary, H. Saadeh, M. Saadeh, M. Yaqub, Flower classification using deep convolutional neural networks, IET Comput. Vision, 12 (2018), 855–862. https://doi.org/10.1049/iet-cvi.2017.0155 2. M. E. Nilsback, A. Zisserman, Automated flower classification over a large number of classes, in 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, (2008), 722–729. https://doi.org/10.1109/ICVGIP.2008.47 3. B. Fernando, E. Fromont, D. Muselet, M. Sebban, Discriminative feature fusion for image classification, in 2012 IEEE Conference on Computer Vision and Pattern Recognition, (2012), 3434–3441. https://doi.org/10.1109/CVPR.2012.6248084 4. A. Angelova, S. Zhu, Efficient object detection and segmentation for fine-grained recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2013), 811–818. 5. H. M. Zawbaa, M. Abbass, S. H. Basha, M. Hazman, A. E. Hassenian, An automatic flower classification approach using machine learning algorithms, in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), (2014), 895–901. https://doi.org/10.1109/ICACCI.2014.6968612
|
|