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Using multispectral uav imagery for marine debris detection in Sri Lanka

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dc.contributor.author Velayuthan, P
dc.contributor.author Piyathilake, V
dc.contributor.author Athapaththu, K
dc.contributor.author Sandaruwan, D
dc.contributor.author Sayakkara, AP
dc.contributor.author Hettiarachchi, H
dc.contributor.editor Piyatilake, ITS
dc.contributor.editor Thalagala, PD
dc.contributor.editor Ganegoda, GU
dc.contributor.editor Thanuja, ALARR
dc.contributor.editor Dharmarathna, P
dc.date.accessioned 2024-02-14T04:30:01Z
dc.date.available 2024-02-14T04:30:01Z
dc.date.issued 2023-12-07
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22215
dc.description.abstract Marine pollution is a significant issue in Sri Lanka, with the country being a major contributor to marine debris. Marine pollution has the potential to adversely impact marine and coastal biodiversity, as well as the fishing and tourism industries. Current methods for monitoring marine debris involve labor-intensive approaches, such as visual surveys conducted from boats or aircraft, beach clean-ups, and underwater transects by divers. However, an emerging trend in many countries is the use of Unmanned Aerial Vehicle (UAV) imagery for monitoring marine debris due to its advantages, including reduced labour requirements, higher spatial resolution, and cost-effectiveness. The work presented in this study utilizes multispectral UAV imagery to monitor marine debris in a coastal area of Ambalangoda, Sri Lanka. For the automated detection of marine debris in captured images, this work replicates the state-of-the-art CutPaste method for region detection and utilized the ResNet-18 model with Faster R-CNN for the final classification of marine debris instances. The implemented approach demonstrated a classification accuracy of approximately 60% in automatic marine debris detection, laying the groundwork for potential enhancements in the future. en_US
dc.language.iso en en_US
dc.publisher Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.subject Marine debris monitoring en_US
dc.subject Unmanned aerial vehicles en_US
dc.subject Multispectral camera en_US
dc.subject Self-supervised learning en_US
dc.subject Anomaly detection en_US
dc.title Using multispectral uav imagery for marine debris detection in Sri Lanka en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2023 en_US
dc.identifier.conference 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.pgnos pp. 1-6 en_US
dc.identifier.proceeding Proceedings of the 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.email vpurushoth97@gmail.com en_US
dc.identifier.email vin@ucsc.cmb.ac.lk en_US
dc.identifier.email kav@ucsc.cmb.ac.lk en_US
dc.identifier.email dsr@ucsc.cmb.ac.lk en_US
dc.identifier.email asa@ucsc.cmb.ac.lk en_US
dc.identifier.email eno@ucsc.cmb.ac.lk en_US


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  • ICITR - 2023 [47]
    International Conference on Information Technology Research (ICITR)

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