RoboWeedSupport -使用全卷积神经网络检测叶片遮挡谷物作物中的杂草位置

M. Dyrmann, R. Jørgensen, H. Midtiby
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引用次数: 91

摘要

本文提出了一种在重度叶遮挡情况下彩色图像中自动检测杂草的方法。采用全卷积神经网络对杂草进行检测。该网络通过安装在全地形车辆上的摄像头收集的冬小麦田地图像中的17,000多个杂草注释进行训练和验证。因此,该网络能够在重度叶遮挡的情况下自动检测出谷类地里的单个杂草实例。
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RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network
This paper presents a method for automating weed detection in colour images despite heavy leaf occlusion. A fully convolutional neural network is used to detect the weeds. The network is trained and validated on a total of more than 17,000 annotations of weeds in images from winter wheat fields, which have been collected using a camera mounted on an all-terrain vehicle. Hereby, the network is able to automatically detect single weed instances in cereal fields despite heavy leaf occlusion.
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Proceedings of the British Society of Animal Science Proceedings of the XIIIth International Symposium on Ruminant Physiology (ISRP 2019) Proceedings of the British Society of Animal Science Proceedings of the Seventeenth Biennial Conference of the Australasian Pig Science Association (APSA) Proceedings of the 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT)
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