SeeCucumbers: using deep learning and drone iagery to detect sea cucumbers on coral reef flats
Li, Joan Y.Q., Duce, Stephanie, Joyce, Karen E., and Xiang, Wei (2021) SeeCucumbers: using deep learning and drone iagery to detect sea cucumbers on coral reef flats. Drones, 5 (2). 28.
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Abstract
Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species.
Item ID: | 69817 |
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Item Type: | Article (Research - C1) |
ISSN: | 2504-446X |
Keywords: | holothurian; remote sensing; UAV; machine learning; object detection; YOLOv3; GreatBarrier Reef; marine ecology; ecological monitoring; FAIR data |
Copyright Information: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Date Deposited: | 02 Nov 2021 01:27 |
FoR Codes: | 40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 50% |
SEO Codes: | 18 ENVIRONMENTAL MANAGEMENT > 1805 Marine systems and management > 180501 Assessment and management of benthic marine ecosystems @ 100% |
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