Robotic detection of marine litter using deep visual detection models

Michael Fulton, Jungseok Hong, Md Jahidul Islam, Junaed Sattar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. This paper evaluates a number of deep-learning algorithms performing the task of visually detecting trash in realistic underwater environments, with the eventual goal of exploration, mapping, and extraction of such debris by using AUVs. A large and publicly-available dataset of actual debris in open-water locations is annotated for training a number of convolutional neural network architectures for object detection. The trained networks are then evaluated on a set of images from other portions of that dataset, providing insight into approaches for developing the detection capabilities of an AUV for underwater trash removal. In addition, the evaluation is performed on three different platforms of varying processing power, which serves to assess these algorithms' fitness for real-time applications.

Original languageEnglish (US)
Title of host publication2019 International Conference on Robotics and Automation, ICRA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5752-5758
Number of pages7
ISBN (Electronic)9781538660263
DOIs
StatePublished - May 2019
Event2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada
Duration: May 20 2019May 24 2019

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2019-May
ISSN (Print)1050-4729

Conference

Conference2019 International Conference on Robotics and Automation, ICRA 2019
CountryCanada
CityMontreal
Period5/20/195/24/19

Bibliographical note

Funding Information:
We gratefully acknowledge the support of the MnDRIVE initiative and the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. The dataset used in this work was created based on the publicly available J-EDI dataset, provided by JAMSTEC, the Japanese Agency for Marine-Earth Science and Technology. The authors wish to thank them for providing this data for scientific endeavors. The authors also wish to thank Sophie Fulton, Marc Ho, Elliott Imhoff, Yuanzhe Liu, Julian Lagman, and Youya Xia for their tireless efforts in annotating images for training purposes.

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