{"title":"卷积神经网络在花粉蜂分类中的应用","authors":"T. Sledevič","doi":"10.1109/AIEEE.2018.8592464","DOIUrl":null,"url":null,"abstract":"The article presents the classification of images with pollen bearing bees using convolutional neural network (CNN). The aim is to find out a sufficient configuration of CNN required for future implementation on low-cost FPGA. A new dataset with bee images was collected on the entrances to several beehives. hidden layers with up to 15 15 x 15 down to 3x3 filter sizes. The CNN configured to three hidden layers 7–7, 5–5, 3–3 was selected for future application as a trade off between accuracy 94% and number of required arithmetic operations.","PeriodicalId":198244,"journal":{"name":"2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"The Application of Convolutional Neural Network for Pollen Bearing Bee Classification\",\"authors\":\"T. Sledevič\",\"doi\":\"10.1109/AIEEE.2018.8592464\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article presents the classification of images with pollen bearing bees using convolutional neural network (CNN). The aim is to find out a sufficient configuration of CNN required for future implementation on low-cost FPGA. A new dataset with bee images was collected on the entrances to several beehives. hidden layers with up to 15 15 x 15 down to 3x3 filter sizes. The CNN configured to three hidden layers 7–7, 5–5, 3–3 was selected for future application as a trade off between accuracy 94% and number of required arithmetic operations.\",\"PeriodicalId\":198244,\"journal\":{\"name\":\"2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIEEE.2018.8592464\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIEEE.2018.8592464","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
摘要
本文介绍了卷积神经网络(CNN)对花粉蜜蜂图像的分类。目的是找出未来在低成本FPGA上实现所需的足够的CNN配置。在几个蜂箱的入口处收集了一个新的蜜蜂图像数据集。隐藏层与多达15 15 x 15到3x3过滤器尺寸。配置为3个隐藏层(7 - 7,5 - 5,3 - 3)的CNN被选择用于未来的应用,以在94%的准确率和所需的算术运算次数之间进行权衡。
The Application of Convolutional Neural Network for Pollen Bearing Bee Classification
The article presents the classification of images with pollen bearing bees using convolutional neural network (CNN). The aim is to find out a sufficient configuration of CNN required for future implementation on low-cost FPGA. A new dataset with bee images was collected on the entrances to several beehives. hidden layers with up to 15 15 x 15 down to 3x3 filter sizes. The CNN configured to three hidden layers 7–7, 5–5, 3–3 was selected for future application as a trade off between accuracy 94% and number of required arithmetic operations.