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
{"title":"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","authors":"M. Dyrmann, R. Jørgensen, H. Midtiby","doi":"10.1017/S2040470017000206","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"5 1","pages":"842-847"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"91","resultStr":"{\"title\":\"RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network\",\"authors\":\"M. Dyrmann, R. Jørgensen, H. Midtiby\",\"doi\":\"10.1017/S2040470017000206\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":7228,\"journal\":{\"name\":\"Advances in Animal Biosciences\",\"volume\":\"5 1\",\"pages\":\"842-847\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"91\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Animal Biosciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/S2040470017000206\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Animal Biosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/S2040470017000206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信