Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
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DOI：
10.1109/icra.2018.8460962
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Andres Milioto;Philipp Lottes;C. Stachniss
中科院分区：
文献类型：
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作者：
Andres Milioto;Philipp Lottes;C. Stachniss
精准农业机器人的目标是减少需要在田间使用的除草剂的数量，必须能够真实的及时识别作物和杂草，以触发除草行动。在本文中，我们解决的问题，基于CNN的语义分割的作物领域分离甜菜植物，杂草，和背景的RGB数据的基础上。我们提出了一个CNN，利用现有的植被指数，并提供了一个真实的时间分类。此外，它可以用相对少量的训练数据有效地重新训练到迄今为止看不见的领域。我们实施和彻底评估我们的系统在真实的农业机器人在不同领域的操作在德国和瑞士。结果表明，该系统具有较好的通用性，工作频率可达20 Hz左右，适合现场在线运行。
Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20 Hz, and is suitable for online operation in the fields.