【摘要】 针对农田异常区域分割问题，采用特征融合跳跃连接模块和全局-局部注意力模块改进UNet网络模型，提出了一种农田异常区域实时分割网络，实现了对多种农田异常区域的精细分割。结果表明，农田生长异常区域实时分割模型的平均交并比（MIoU）明显优于其他模型，平均交并比为41.24%；相较于使用UNet作为基线的模型，虽然本研究模型的参数量略有增加，但农田分割效果明显提升，MIoU提高了4.16个百分点；与基于Transformer编码器的SegFormer模型相比，本研究模型的参数量基本相同，MIoU提高了2.50个百分点。本研究模型通过采用自适应采样训练方法确保在每个类别上都能取得出色的分割效果。利用多光谱航空图像训练农田生长异常区域实时分割模型，有助于实现无人机对农田生长进行实时监测、预警，推动智慧农业发展进程，为自动监测农田生长情况提供了新的方法和思路。更多还原

【Abstract】 In response to the problem of abnormal segmentation in farmland,a feature fusion skip connection module and a global-local attention module were used to improve the UNet network model. A real-time segmentation network for abnormal farmland areas was proposed,which achieved fine segmentation of various abnormal farmland areas. The results showed that the Mean Intersection Union ratio(MIoU)of the real-time segmentation model for abnormal growth areas in farmland was significantly better than that of other models,with a MIoU of 41.24%;compared to the model using UNet as the baseline,although the number of parameters in this study model had slightly increased,the farmland segmentation effect had significantly improved,with an increase of 4.16 percentage points in MIoU;compared with the SegFormer model based on Transformer encoder,the parameter count of this study model was basically the same,with an increase of 2.50 percentage points in MIoU. This research model ensured excellent segmentation performance in each category by using adaptive sampling training methods. Using multispectral aerial images to train a real-time segmentation model for abnormal growth areas in farmland could help achieve real-time monitoring and early warning of farmland growth by drones,promote the development of smart agriculture,and provide new methods and ideas for automatic monitoring of farmland growth.更多还原