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Article

Using YOLOv5, SAHI, and GIS with Drone Mapping to Detect Giant Clams on the Great Barrier Reef

1
College of Science and Engineering, James Cook University, Bebegu Yumba Campus, Townsville, QLD 4811, Australia
2
College of Science and Engineering/TropWATER, James Cook University, Nguma-Bada Campus, Cairns, QLD 4879, Australia
*
Author to whom correspondence should be addressed.
Drones 2024, 8(9), 458; https://doi.org/10.3390/drones8090458
Submission received: 24 July 2024 / Revised: 30 August 2024 / Accepted: 30 August 2024 / Published: 3 September 2024
(This article belongs to the Section Drones in Ecology)

Abstract

Despite the ecological importance of giant clams (Tridacninae), their effective management and conservation is challenging due to their widespread distribution and labour-intensive monitoring methods. In this study, we present an alternative approach to detecting and mapping clam density at Pioneer Bay on Goolboddi (Orpheus) Island on the Great Barrier Reef using drone data with a combination of deep learning tools and a geographic information system (GIS). We trained and evaluated 11 models using YOLOv5 (You Only Look Once, version 5) with varying numbers of input image tiles and augmentations (mean average precision—mAP: 63–83%). We incorporated the Slicing Aided Hyper Inference (SAHI) library to detect clams across orthomosaics, eliminating duplicate counts of clams straddling multiple tiles, and further, applied our models in three other geographic locations on the Great Barrier Reef, demonstrating transferability. Finally, by linking detections with their original geographic coordinates, we illustrate the workflow required to quantify animal densities, mapping up to seven clams per square meter in Pioneer Bay. Our workflow brings together several otherwise disparate steps to create an end-to-end approach for detecting and mapping animals with aerial drones. This provides ecologists and conservationists with actionable and clear quantitative and visual insights from drone mapping data.
Keywords: YOLOv5; GIS; giant clam; wildlife monitoring; SAHI; deep learning; drone; artificial intelligence; geospatial analysis YOLOv5; GIS; giant clam; wildlife monitoring; SAHI; deep learning; drone; artificial intelligence; geospatial analysis

Share and Cite

MDPI and ACS Style

Decitre, O.; Joyce, K.E. Using YOLOv5, SAHI, and GIS with Drone Mapping to Detect Giant Clams on the Great Barrier Reef. Drones 2024, 8, 458. https://doi.org/10.3390/drones8090458

AMA Style

Decitre O, Joyce KE. Using YOLOv5, SAHI, and GIS with Drone Mapping to Detect Giant Clams on the Great Barrier Reef. Drones. 2024; 8(9):458. https://doi.org/10.3390/drones8090458

Chicago/Turabian Style

Decitre, Olivier, and Karen E. Joyce. 2024. "Using YOLOv5, SAHI, and GIS with Drone Mapping to Detect Giant Clams on the Great Barrier Reef" Drones 8, no. 9: 458. https://doi.org/10.3390/drones8090458

APA Style

Decitre, O., & Joyce, K. E. (2024). Using YOLOv5, SAHI, and GIS with Drone Mapping to Detect Giant Clams on the Great Barrier Reef. Drones, 8(9), 458. https://doi.org/10.3390/drones8090458

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