A Real-time Edge-AI System for Reef Surveys

• URL: http://arxiv.org/abs/2208.00598v1
• Date: Mon, 1 Aug 2022 04:06:14 GMT
• Title: A Real-time Edge-AI System for Reef Surveys
• Authors: Yang Li, Jiajun Liu, Brano Kusy, Ross Marchant, Brendan Do, Torsten Merz, Joey Crosswell, Andy Steven, Lachlan Tychsen-Smith, David Ahmedt-Aristizabal, Jeremy Oorloff, Peyman Moghadam, Russ Babcock, Megha Malpani, Ard Oerlemans
• Abstract summary: Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef. We present a comprehensive real-time machine learning-based underwater data collection and curation system on edge devices for COTS monitoring.
• Score: 6.070670469403929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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