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A deep active learning system for species identification and counting in camera trap images
[article]
2019
arXiv
pre-print
We combine the power of machine intelligence and human intelligence to build a scalable, fast, and accurate active learning system to minimize the manual work required to identify and count animals in ...
Motion-activated cameras, also known as camera traps, are a critical tool for population surveys, as they are cheap and non-intrusive. ...
For example, species identification in camera trap images is an image classification problem in which the input is the camera trap image and the output is the probability of the presence of each species ...
arXiv:1910.09716v1
fatcat:xqrxsaf5obbqxpksictfildjwa
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
2018
Proceedings of the National Academy of Sciences of the United States of America
We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 millionimage Snapshot Serengeti dataset. ...
Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. ...
We thank Sarah Benson-Amram, the SS volunteers, and the members of the Evolving AI Laboratory at the University of Wyoming for valuable feedback, especially Joost Huizinga, Tyler Jaszkowiak, Roby Velez ...
doi:10.1073/pnas.1719367115
pmid:29871948
pmcid:PMC6016780
fatcat:uaflbasnznbzfescvn2vw4bnl4
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
[article]
2017
arXiv
pre-print
We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2-million-image Snapshot Serengeti dataset. ...
More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that ...
Deep neural networks can successfully identify, count, and describe animals in camera-trap images. ...
arXiv:1703.05830v5
fatcat:5ip5ine4czhshieovcue2ellli
Automatic Detection and Monitoring of Insect Pests—A Review
2020
Agriculture
The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications ...
Future trends of automatic traps and decision support systems are also discussed. ...
[36] developed a model for the counting and identification of aphids based on machine learning and an adapted smartphone. ...
doi:10.3390/agriculture10050161
fatcat:jilapwwc5ncexnoj3u3nblcd3q
Deep Learning Object Detection Methods for Ecological Camera Trap Data
[article]
2018
arXiv
pre-print
Recent advances in the field of deep learning for object detection show promise towards automating the analysis of camera trap images. ...
Deep learning methods for computer vision tasks show promise for automating the data analysis of camera trap images. ...
ACKNOWLEDGMENTS The authors would like to thank all the Snapshot Serengeti volunteers and the camera trap community at large for uploading their data for public access. ...
arXiv:1803.10842v1
fatcat:5ptcazjymjgfdbc3qairowsyjy
Deep learning and computer vision will transform entomology
2021
Proceedings of the National Academy of Sciences of the United States of America
Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. ...
Advances in computer vision and deep learning provide potential new solutions to this global challenge. ...
Other solutions have embedded a digital camera and a microprocessor that can count trapped individuals in real time using object detection based on a deep learning model (37) . ...
doi:10.1073/pnas.2002545117
pmid:33431561
fatcat:3m4xtz5365awlpv6nlmunkuwl4
Three critical factors affecting automated image species recognition performance for camera traps
2020
Ecology and Evolution
The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. ...
Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify ...
their own-deep learning systems for camera trap images (Schneider github animal classification tool) ...
doi:10.1002/ece3.6147
pmid:32274005
pmcid:PMC7141055
fatcat:m3mabgop2zf2dp7dsogprm3rui
Image-Based Animal Detection and Breed Identification Using Neural Networks
2020
Journal of science and technolgy
to the world of ecology and trap camera images analysis . ...
of filtering animal images and identifying species automatically and counting the number of species. ...
We also thank all friends for being a constant source of our support. ...
doi:10.46243/jst.2020.v5.i5.pp130-134
fatcat:rf2tlgi6y5edfbqrwq6q4heo34
Deep learning and computer vision will transform entomology
[article]
2020
bioRxiv
pre-print
Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. ...
Advances in computer vision and deep learning provide potential new solutions to this global challenge. ...
and identification of species recorded with cameras in the field pose a 332 critical challenge for implementing deep learning tools in entomology. ...
doi:10.1101/2020.07.03.187252
fatcat:wv3sn4jet5dr3llion5ua34ssu
A light trap and computer vision system to detect and classify live moths (Lepidoptera) using tracking and deep learning
[article]
2020
bioRxiv
pre-print
A computer vision algorithm referred to as Moth Classification and Counting, based on deep learning analysis of the captured images then tracked and counted the number of insects and identified moth species ...
A light trap with multiple illuminations and a camera was designed to attract and monitor live insects during twilight and night hours. ...
Developing a deep learning model for classifying species was an iterative process with an alternation between selecting images and training CNN models. ...
doi:10.1101/2020.03.18.996447
fatcat:lsxwsgqk6rh55jlft4kvu2obna
An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning
2021
Sensors
A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth ...
Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. ...
Acknowledgments: The authors like to acknowledge Freia Martensen for language and proof reading the article and Mads Dyrmann for inputs and review. ...
doi:10.3390/s21020343
pmid:33419136
fatcat:g4nxswllofc6vhpmd4l2qrd5hy
Perspectives on individual animal identification from biology and computer vision
[article]
2021
arXiv
pre-print
We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future. ...
In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. ...
Support for MV, BR, NW, and BPH was provided by Alaska Education Tax ...
arXiv:2103.00560v1
fatcat:6xdsiojn7vamxonhwmklse3tja
Perspectives on individual animal identification from biology and computer vision
2021
Integrative and Comparative Biology
We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future. ...
In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. ...
Images from camera traps can be used both for model training and monitored for inference. ...
doi:10.1093/icb/icab107
pmid:34050741
pmcid:PMC8490693
fatcat:zp32mrr56fcvda4ubc3r2xotja
Seeing biodiversity: perspectives in machine learning for wildlife conservation
[article]
2021
arXiv
pre-print
We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. ...
approaches and train a new generation of data scientists in ecology and conservation. ...
SB would like to thank the Microsoft AI for Earth initiative, the Idaho Department of Fish and Game, and Wildlife Protection Solutions for insightful discussions and providing data for figures. ...
arXiv:2110.12951v1
fatcat:hzuz5aeja5dfxpte6xprg3yjti
Iterative Human and Automated Identification of Wildlife Images
[article]
2021
arXiv
pre-print
These two drawbacks can be overcome through a hybrid combination of machine learning and humans in the loop. ...
Our proposed iterative human and automated identification approach is capable of learning from wildlife imagery data with a long-tailed distribution. ...
Automatic image recognition systems The use of deep learning (a subset of AI technology) to automatically identify animals in camera trap images has recently drawn considerable attention from the ecological ...
arXiv:2105.02320v2
fatcat:ja3wbtfkpbedrinyfxlgnsmuui
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