Abstract: Paddy weed position acquisition is the basis of targeting spraying herbicide and mechanical intelligence weeding. In order to acquire information acquisition of weeds in seedling stage under natural light environment and complex paddy field background, in this paper, a new semantic segmentation method for weeds at seedling stage was proposed based on the full convolutional neural network and DeepLabV3+ was used for semantic segmentation of seedlings and weeds to obtain the weed location information by sagittaria trifolia as the research object. Firstly, the weeds RGB image of sagittaria trifolia in the paddy field were captured, and each pixel of the seedlings, weeds and background in the images were manually labeled by the image labeling tool LabelMe. 70% data set was used for the parameter training of the DeepLabV3+ network model, and 30% data set was used to test the performance of DeepLabV3+. By comparing with FCN and U-Net semantic segmentation methods, the proposed DeepLabV3+ semantic segmentation method had the best performance indicators, such as accuracy, proportional ratio, frequency weight intersection ratio and F value, and the experiment results showed that the pixel accuracy of DeepLabV3+ model was up to 922%. The accuracy rates of U-Net and FCN methods were 92.1% and 84.7% respectively. The method proposed in this paper could accurately segment weeds, seedlings and background pixels at seedling stage of paddy field, and also meet the practical application requirements of intelligent weeding and targeted herbicide spraying.

Key words: paddy weed, sagittaria trifolia, semantic segmentation, DeepLabV3+

摘要： 稻田杂草位置获取是靶向喷施除草剂和机械智能除草的基础，为实现自然光照环境和水田复杂背景下稻田苗期杂草的信息获取。以稻田恶性杂草野慈姑为研究对象，提出一种基于全卷积神经网络的稻田苗期杂草语义分割方法，利用DeepLabV3+对秧苗和杂草进行语义分割进而获取的杂草位置信息。首先人工田间采集稻田苗期杂草野慈姑的RGB图像，通过图像标注工具LabelMe人工标注图像中秧苗、杂草和背景的各个像素点，70%数据集用于DeepLabV3+网络模型参数的训练，30%数据集用于测试DeepLabV3+性能。然后与FCN和U-Net两种语义分割方法进行比较，所提出的DeepLabV3+语义分割方法准确率、均正比、频权交并比和F值等性能指标都最优，试验得出：DeepLabV3+模型像素准确率最高达到92.2%，高于U-Net和FCN方法的准确率92.1%和84.7%。所提出的方法能对稻田苗期杂草、秧苗和背景像素进行准确分割，满足智能除草和除草剂靶向喷施的实际应用需求。

关键词: 稻田杂草, 野慈姑, 语义分割, DeepLabV3+