In order to extract the lodging area timely and accurately, a lodging area extraction model, namely Attention_U2-Net, was proposed. By integrating multi-scale features and based on U2-Net, Attention_U2-Net employed non-local attention mechanism to replace the hole convolution with large step size, expanded the receptive field of high-level network and improved the recognition accuracy of ground objects with different sizes, and utilized channel attention mechanism to improve the cascade mode and enhanced the accuracy. A multi-level joint weighted loss function was designed to balance the difficult and easy samples, and solve the challenge of imbalance between positive and negative samples. Patch-based pipelines were utilized to extract the lodging area. Experimental results on the self-built dataset showed effectiveness of Attention_U2-Net. The precision rate was 86.53%, the recall rate was 89.42%, and the F1 value was 87.95%, respectively. Compared with FastFCN, U-Net, U2-Net, FCN, SegNet and DeepLabv3, Attention_U2-Net achieved the highest F1 value and showed strong robustness and extraction accuracy. Compared with the labeled area, the extracted area obtained by Attention_U2-Net via cropping method was the closest one, and the accuracy rate of lodging area can reach 97.25%. Meanwhile, the false detection area of Attention_U2-Net was the smallest among all models. Experimental results showed that Attention_U2-Net had strong robustness and high segmentation accuracy, which can be utilized as a valuable reference for UAV remote sensing of wheat affected area and loss assessment.