{"title":"基于图像的动物检测和品种识别的神经网络","authors":"","doi":"10.46243/jst.2020.v5.i5.pp130-134","DOIUrl":null,"url":null,"abstract":"Having accurate, detailed, and up-to-date information about the behaviour of animals in the wild world would\nimprove our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively\ncollect such data through various sources, which could help catalyse the transformation of many fields of ecology, wildlife\nbiology, zoology, conservation biology, animal behaviour into “big data” sciences and many more. So extracting information\nfrom the pictures remains an expensive, time-consuming, and manual task for us. We demonstrate that such information can be\nautomatically extracted by deep learning and convolutional neural network. Leveraging on recent advances in deep learning\ntechniques in computer vision, we propose in this project a framework to build automated animal recognition in the wild,\naiming at an automated wildlife monitoring system. In particular, we use a single-labelled dataset done by citizen scientists,\nand the state-of-the-art deep convolutional neural network architectures, face biometrics, to train a computational system\ncapable of filtering animal images and identifying species automatically and counting the number of species. Our results\nsuggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of\ninformation about vast numbers of animals in the wild and this, in turn, can, therefore, speed up research findings, construct\nmore efficient citizen science-based monitoring systems and subsequent management decisions, having the potential to make\nsignificant impacts to the world of ecology and trap camera images analysis .","PeriodicalId":23534,"journal":{"name":"Volume 5, Issue 4","volume":"24 14","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Image-Based Animal Detection and Breed Identification Using Neural\\nNetworks\",\"authors\":\"\",\"doi\":\"10.46243/jst.2020.v5.i5.pp130-134\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Having accurate, detailed, and up-to-date information about the behaviour of animals in the wild world would\\nimprove our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively\\ncollect such data through various sources, which could help catalyse the transformation of many fields of ecology, wildlife\\nbiology, zoology, conservation biology, animal behaviour into “big data” sciences and many more. So extracting information\\nfrom the pictures remains an expensive, time-consuming, and manual task for us. We demonstrate that such information can be\\nautomatically extracted by deep learning and convolutional neural network. Leveraging on recent advances in deep learning\\ntechniques in computer vision, we propose in this project a framework to build automated animal recognition in the wild,\\naiming at an automated wildlife monitoring system. In particular, we use a single-labelled dataset done by citizen scientists,\\nand the state-of-the-art deep convolutional neural network architectures, face biometrics, to train a computational system\\ncapable of filtering animal images and identifying species automatically and counting the number of species. Our results\\nsuggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of\\ninformation about vast numbers of animals in the wild and this, in turn, can, therefore, speed up research findings, construct\\nmore efficient citizen science-based monitoring systems and subsequent management decisions, having the potential to make\\nsignificant impacts to the world of ecology and trap camera images analysis .\",\"PeriodicalId\":23534,\"journal\":{\"name\":\"Volume 5, Issue 4\",\"volume\":\"24 14\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 5, Issue 4\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46243/jst.2020.v5.i5.pp130-134\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 5, Issue 4","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46243/jst.2020.v5.i5.pp130-134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image-Based Animal Detection and Breed Identification Using Neural
Networks
Having accurate, detailed, and up-to-date information about the behaviour of animals in the wild world would
improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively
collect such data through various sources, which could help catalyse the transformation of many fields of ecology, wildlife
biology, zoology, conservation biology, animal behaviour into “big data” sciences and many more. So extracting information
from the pictures remains an expensive, time-consuming, and manual task for us. We demonstrate that such information can be
automatically extracted by deep learning and convolutional neural network. Leveraging on recent advances in deep learning
techniques in computer vision, we propose in this project a framework to build automated animal recognition in the wild,
aiming at an automated wildlife monitoring system. In particular, we use a single-labelled dataset done by citizen scientists,
and the state-of-the-art deep convolutional neural network architectures, face biometrics, to train a computational system
capable of filtering animal images and identifying species automatically and counting the number of species. Our results
suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of
information about vast numbers of animals in the wild and this, in turn, can, therefore, speed up research findings, construct
more efficient citizen science-based monitoring systems and subsequent management decisions, having the potential to make
significant impacts to the world of ecology and trap camera images analysis .