Li Shangze, Liu Shen, Ji Mingyu, Cao Yuhao, Yun Bai
Aulin College, Northeast Forestry University, Harbin, China.
College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
Front Plant Sci. 2024 Dec 20;15:1471705. doi: 10.3389/fpls.2024.1471705. eCollection 2024.
Addressing the issues with insufficient multi-scale feature perception and incomplete understanding of global information in traditional convolutional neural networks for image classification of wheat leaf disease, this paper proposes a global local feature network, i.e. GLNet, which adopts a unique global-local convolutional neural network architecture, realizes the comprehensive capturing of multi-scale features in an image by processing the global feature block and local feature block in parallel and integrating the information of both of them with the help of a feature fusion block. By processing global and local feature blocks in parallel and integrating the information of both effectively with the help of feature fusion blocks, the model realizes the comprehensive capture of multi-scale features in images. This innovative design significantly enhances the model ability to understand the features of wheat leaf disease images, and thus demonstrates excellent performance and accuracy in the task of classifying wheat leaf disease images in real-world scenarios. The successful application of GLNet provides new ideas and effective tools for solving complex image classification problems.
针对传统卷积神经网络在小麦叶部病害图像分类中存在多尺度特征感知不足和对全局信息理解不完整的问题，本文提出了一种全局局部特征网络，即GLNet，它采用独特的全局-局部卷积神经网络架构，通过并行处理全局特征块和局部特征块，并借助特征融合块整合两者信息，实现对图像多尺度特征的全面捕捉。通过并行处理全局和局部特征块，并在特征融合块的帮助下有效整合两者信息，该模型实现了对图像多尺度特征的全面捕捉。这种创新设计显著增强了模型理解小麦叶部病害图像特征的能力，从而在实际场景中的小麦叶部病害图像分类任务中表现出优异的性能和准确性。GLNet的成功应用为解决复杂图像分类问题提供了新思路和有效工具。
