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A Real-time Edge-AI System for Reef Surveys

1 August 2022
Yongqian Li
Jiajun Liu
Brano Kusy
Ross Marchant
Brendan Do
T. Merz
Joey Crosswell
Andrew D. L. Steven
Lachlan Tychsen-Smith
David Ahmedt-Aristizabal
Jeremy Oorloff
Peyman Moghadam
R. Babcock
Megha Malpani
Ard A. J. Oerlemans
ArXiv (abs)PDFHTML
Abstract

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels. In this paper, we present a comprehensive real-time machine learning-based underwater data collection and curation system on edge devices for COTS monitoring. In particular, we leverage the power of deep learning-based object detection techniques, and propose a resource-efficient COTS detector that performs detection inferences on the edge device to assist marine experts with COTS identification during the data collection phase. The preliminary results show that several strategies for improving computational efficiency (e.g., batch-wise processing, frame skipping, model input size) can be combined to run the proposed detection model on edge hardware with low resource consumption and low information loss.

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