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@software{Jocher:2020,
author = {Glenn Jocher and
Alex Stoken and
Jirka Borovec and
NanoCode012 and
ChristopherSTAN and
Liu Changyu and
Laughing and
tkianai and
Adam Hogan and
lorenzomammana and
yxNONG and
AlexWang1900 and
Laurentiu Diaconu and
Marc and
wanghaoyang0106 and
ml5ah and
Doug and
Francisco Ingham and
Frederik and
Guilhen and
Hatovix and
Jake Poznanski and
Jiacong Fang and
Lijun Yu 于力军 and
changyu98 and
Mingyu Wang and
Naman Gupta and
Osama Akhtar and
PetrDvoracek and
Prashant Rai},
title = {{ultralytics/yolov5: v3.1 - Bug Fixes and
Performance Improvements}},
month = oct,
year = 2020,
publisher = {Zenodo},
version = {v3.1},
doi = {10.5281/zenodo.4154370},
url = {https://doi.org/10.5281/zenodo.4154370}
}
@TECHREPORT{Moore:2020,
title = "California Trash Monitoring Methods and Assessments Playbook",
author = "Moore, Shelly and Hale, Tony and Weisberg, Stephen B and
Flores, Lorenzo and Kauhanen, Pete",
institution = "San Francisco Estuary Institute",
year = 2020
}
@ARTICLE{Hapich:2022,
title = "Trash Taxonomy Tool: harmonizing classification systems used to
describe trash in environments",
author = "Hapich, Hannah and Cowger, Win and Gray, Andrew and Tangri, Neil
and Hale, Tony and Magdy, Amr and Vermilye, Antoinette and Yu,
Walter and Ayres, Dick and Moore, Charles and Vermilye, John and
Singh, Samiksha and Haiman, Aaron N K and Youngblood, Kathryn and
Kang, Yunfan and McCauley, Margaret and Lok, Trevor and Moore,
Shelly and Baggs, Eric and Lippiatt, Sherry and Kohler, Peter and
Conley, Gary and Taing, Janna and Mock, Jeremiah",
abstract = "Despite global efforts to monitor, mitigate against, and prevent
trash (mismanaged solid waste) pollution, no harmonized trash
typology system has been widely adopted worldwide. This impedes
the merging of datasets and comparative analyses. We addressed
this problem by 1) assessing the state of trash typology and
comparability, 2) developing a standardized and harmonized
framework of relational tables and tools, and 3) informing
practitioners about challenges and potential solutions. We
analyzed 68 trash survey lists to assess similarities and
differences in classification. We created comprehensive
harmonized hierarchical tables and alias tables for item and
material classes. On average, the 68 survey lists had 20.8\% of
item classes in common and 29.9\% of material classes in common.
Multiple correspondence analysis showed that the 68 surveys were
not significantly different regarding organization type,
ecosystem focus, or substrate focus. We built the Trash Taxonomy
Tool (TTT) web-based application with query features and open
access at openanalysis.org/trashtaxonomy. The TTT can be applied
to improve, create, and compare trash surveys, and provides
practitioners with tools to integrate datasets and maximize
comparability. The use of TTT will ultimately facilitate
improvements in assessing trends across space and time,
identifying targets for mitigation, evaluating the effectiveness
of prevention measures, informing policymaking, and holding
producers responsible.",
journal = "Microplastics and Nanoplastics",
volume = 2,
number = 1,
pages = "15",
month = jun,
year = 2022,
doi = "10.1186/s43591-022-00035-1"
}
@misc{Waterboards:2018,
author = {Waterboards},
title = {Trash Tracker},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/CAWaterBoardDataCenter/Trash-Tracker}
}
@article{vanLieshout:2020,
author = {van Lieshout, Colin and van Oeveren, Kees and van Emmerik, Tim and Postma, Eric},
title = {Automated River Plastic Monitoring Using Deep Learning and Cameras},
journal = {Earth and Space Science},
volume = {7},
number = {8},
pages = {e2019EA000960},
keywords = {plastic pollution, object detection, automated monitoring, deep learning, artificial intelligence, river plastic},
doi = {10.1029/2019EA000960},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019EA000960},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019EA000960},
note = {e2019EA000960 10.1029/2019EA000960},
year = {2020}
}
@ARTICLE{Lynch:2018,
title = "{OpenLitterMap.com} -- Open Data on Plastic Pollution with
Blockchain Rewards (Littercoin)",
author = "Lynch, Se{\'a}n",
abstract = "OpenLitterMap rewards users with Littercoin for producing open
data on litter. Open data on the geospatial characteristics of
litter provide means of invoking and evaluating responses to
plastic pollution. OpenLitterMap currently works as a web app on
all devices with native mobile apps in development. The stack
includes the integration of the Laravel PHP Framework on the
backend; Vue for frontend reactivity; NativeScript-Vue for mobile
apps; Bulma for CSS; Leaflet for web-mapping; Turf.js for
geospatial analysis; the Ethereum Blockchain for tokenization;
Stripe; ChartJS; AWS; and more. Anywhere from a single cigarette
butt to the contents of an entire beach or street clean can be
logged in a single geotagged photo. Alternatively, a simple index
may be used if litter is incalculable. The open data includes an
increasing 100+ pre-defined types of litter; 20+ corporate
brands; verification status; coordinates; timestamp; phone model;
the latest OpenStreetMap address at each location; and the litter
presence as a Boolean. To date, 100\% of all submitted data (~
8200 photos, ~ 28,000 litter from over 150 contributors) has been
manually verified which is being used to develop machine learning
algorithms.",
journal = "Open Geospatial Data, Software and Standards",
volume = 3,
number = 1,
pages = "6",
month = jun,
year = 2018,
doi = "10.1186/s40965-018-0050-y"
}
@misc{WADEAI:2020,
author = {K. Kerge, W. Cowger, K. Haamer, K. Ehala, K. Kivistik, T. Tammiste, M. Vares},
title = {WADE AI Trash Detection},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/letsdoitworld/wade-ai}
}
@misc{Wuu:2018,
author = {S. Wuu},
title = {Litter Detection Tensorflow},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/isaychris/litter-detection-tensorflow}
}
@article{Majchrowska:2022,
title = {Deep learning-based waste detection in natural and urban environments},
journal = {Waste Management},
volume = {138},
pages = {274-284},
year = {2022},
issn = {0956-053X},
doi = {10.1016/j.wasman.2021.12.001},
url = {https://www.sciencedirect.com/science/article/pii/S0956053X21006474},
author = {Sylwia Majchrowska and Agnieszka Mikołajczyk and Maria Ferlin and Zuzanna Klawikowska and Marta A. Plantykow and Arkadiusz Kwasigroch and Karol Majek},
keywords = {Object detection, Semi-supervised learning, Waste classification benchmarks, Waste detection benchmarks, Waste localization, Waste recognition},
abstract = {Waste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, both for economic and ecological reasons, and the industry demands high efficiency. Current studies towards automatic waste detection are hardly comparable due to the lack of benchmarks and widely accepted standards regarding the used metrics and data. Those problems are addressed in this article by providing a critical analysis of over ten existing waste datasets and a brief but constructive review of the existing Deep Learning-based waste detection approaches. This article collects and summarizes previous studies and provides the results of authors’ experiments on the presented datasets, all intended to create a first replicable baseline for litter detection. Moreover, new benchmark datasets detect-waste and classify-waste are proposed that are merged collections from the above-mentioned open-source datasets with unified annotations covering all possible waste categories: bio, glass, metal and plastic, non-recyclable, other, paper, and unknown. Finally, a two-stage detector for litter localization and classification is presented. EfficientDet-D2 is used to localize litter, and EfficientNet-B2 to classify the detected waste into seven categories. The classifier is trained in a semi-supervised fashion making the use of unlabeled images. The proposed approach achieves up to 70% of average precision in waste detection and around 75% of classification accuracy on the test dataset. The code and annotations used in the studies are publicly available online11https://github.com/wimlds-trojmiasto/detect-waste..}
}
@misc{Proença:2020,
doi = {10.48550/ARXIV.2003.06975},
url = {https://arxiv.org/abs/2003.06975},
author = {Proença, Pedro F and Simões, Pedro},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {TACO: Trash Annotations in Context for Litter Detection},
publisher = {arXiv},
year = {2020},
copyright = {arXiv.org perpetual, non-exclusive license}
}