A real-time edge-AI system for reef surveys
Description
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.
Impact and interest:
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ID Code: | 236357 | ||||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||
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Measurements or Duration: | 4 pages | ||||
Keywords: | detection dataset, real-time object detection and tracking | ||||
DOI: | 10.1145/3495243.3558278 | ||||
ISBN: | 978-1-4503-9181-8 | ||||
Pure ID: | 117640674 | ||||
Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics |
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Copyright Owner: | 2022 Association for Computing Machinery | ||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||
Deposited On: | 16 Nov 2022 06:20 | ||||
Last Modified: | 26 Jul 2024 13:53 |
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