【摘要】 针对传统方法进行图像分割易受噪声影响的问题,提出一种基于U-Net网络的无人机图像语义分割网络模型。该模型不需要对图像进行预处理,利用反卷积恢复图像分辨率,采用U型结构连接低层网络和高层网络的特征图,利用跳跃连接降低网络复杂度,同时使用Dropout正则化随机激活网络隐藏单元以防止过拟合。实验结果表明:该网络模型可以自动定位林木信息,准确分割林木区域,进一步优化边缘分割结果,实现端对端的图像分割。该模型具有良好的泛化能力,在其他图像分割领域也具有应用价值。更多还原

【Abstract】 Aiming at the problem that traditional image segmentation is susceptible to noise, a U-Net network-based semantic segmentation network model for UAV images is proposed. The model does not need to preprocess the image, uses deconvolution to restore the image resolution, uses a U-shaped structure to connect the feature maps of the low-level network and the high-level network, uses skip connection to reduce network complexity, and uses Dropout regularization to randomly activate network hiding Unit to prevent overfitting. The experimental results show that the network model can automatically locate forest information, accurately segment the forest area, further optimize the edge segmentation results, and achieve end-to-end image segmentation. The model has good generalization ability and has application value in other image segmentation fields.更多还原