{"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}
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.