Zeng Zhigao, Huang Cheng, Zhu Wenqiu, Wen Zhiqiang, Yuan Xinpan
School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan 412007, China.
Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Zhuzhou, Hunan 412007, China.
Math Biosci Eng. 2023 Jun 19;20(8):13900-13920. doi: 10.3934/mbe.2023619.
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.
为了解决基于深度学习的花卉图像分类方法在早期特征提取过程中丢失更多特征信息以及模型占用更多存储空间的问题，本文提出了一种基于多尺度特征融合和注意力机制的新型轻量级神经网络模型。首先，选择AlexNet模型作为基本框架。其次，使用多尺度特征融合模块（MFFM）来替代浅层单尺度卷积。MFFM包含三个不同大小的深度可分离卷积分支，可以融合不同尺度的特征并减少单尺度卷积导致的特征损失。第三，首先添加两层改进的Inception模块以增强深度特征的提取，并添加一层混合注意力模块以在后期加强模型对关键信息的聚焦程度。最后，使用全局平均池化和全连接层的组合来完成花卉图像分类。实验结果表明，我们的轻量级模型比基线模型具有更少参数、占用更少存储空间且具有更高的分类准确率，这有助于在移动设备上实现更准确的花卉图像识别。
