Weeds are harmful to crop yield. The segmentation of weeds in images is of great significance for precise weeding and reducing herbicide pollution. However, in the field environment, crops and weeds are similar, so it is difficult to accurately segment weed from complex field images. In this paper, an algorithm based on deep learning was proposed to segment weeds from images. This algorithm can segment weeds from the soil and crops in images. This semantic segmentation algorithm was developed with a simplified U-net. Due to the difficulty of image labeling for the semantic segmentation of weeds, an image augmentation method was proposed. The semantic segmentation network was trained by a two-stage training method composed of pre-training and fine-tuning. After training, the intersection over union (IoU) of this method was 92.91% and the average segmentation time of a single image (ST) was 51.71 ms. The results demonstrated that the modified U-Net was able to effectively segment weeds from images with a significant amount of other plants. The weed-targeted image segmentation method proposed in this paper can accurately segment weeds in complex field environments. It has a relatively wide range of applicability.

杂草对作物产量有害。图像中杂草的分割对于精确除草和减少除草剂污染具有重要意义。然而，在田间环境中，农作物和杂草相似，因此很难从复杂的田间图像中准确地分割出杂草。在本文中，提出了一种基于深度学习的算法来从图像中分割杂草。该算法可以从图像中的土壤和作物中分割杂草。这种语义分割算法是用简化的 U-net 开发的。针对杂草语义分割的图像标注困难，提出了一种图像增强方法。语义分割网络采用预训练和微调组成的两阶段训练方法进行训练。经过训练，该方法的交集（IoU）为92。91%，单张图像（ST）的平均分割时间为 51.71 ms。结果表明，修改后的 U-Net 能够有效地从具有大量其他植物的图像中分割杂草。本文提出的以杂草为目标的图像分割方法可以准确分割复杂田间环境中的杂草。它的适用范围比较广。