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基于改进USE-Net网络的林木图像语义分割研究

Research on Tree Image Semantic Segmentation Based on Improved USE-Net Network

  • 摘要: 为进一步准确定位林木信息、分割林木区域以及实时检测森林资源动态变化,提出一种基于改进USE-Net卷积神经网络的林木图像语义分割模型。该模型在U-Net网络基础上,添加SE注意力模块在网络的过渡层,以显式建模林木特征通道间的相互依赖关系,突出特定林木分割特征并抑制无关区域。实验结果表明,U型结构和SE注意力模块的引入使得改进USE-Net网络在处理模糊林木边界等方面具有优势,能够准确分割林木区域,在智能科学管理森林资源领域具有理论价值和应用价值。

     

    Abstract: To further precisely locate forest tree information, segment forest areas and detect dynamic changes in forest resources in real-time, a semantic segmentation model of tree images based on an improved USE-Net convolutional neural network is proposed. Based on the U-Net network, the model adds SE attention module to the transition layer of the network to explicitly model the interdependence between forest tree feature channels, highlight specific forest tree segmentation features and suppress irrelevant regions. The experimental results indicate that the U-shaped structure and the introduction of the SE attention module make the improved USE-Net network have significant advantages in dealing with fuzzy forest boundaries and other aspects, and can accurately segment forest tree areas, which has theoretical value and applications in the field of intelligent scientific management of forest resources.

     

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