Deep learning for automated coral reef monitoring a novel system based on YOLOv8 detection and DeepSORT tracking
Coral reefs are vital for biodiversity, coastal protection, food security, and tourism, yet they face severe threats from anthropogenic activities and climate change, which are leading to their decline. Effective coral reef monitoring is essential for ecological understanding and conservation, but t...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-11-01
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| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125001797 |
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| Summary: | Coral reefs are vital for biodiversity, coastal protection, food security, and tourism, yet they face severe threats from anthropogenic activities and climate change, which are leading to their decline. Effective coral reef monitoring is essential for ecological understanding and conservation, but traditional methods are resource-intensive and rely on experts. To address these challenges, we present an automated, deep learning-based monitoring system that integrates YOLOv8, a state-of-the-art object detection algorithm, with DeepSORT, a robust multi-object tracking method, to identify and track coral formations in underwater video footage. Our system was fine-tuned using two curated and annotated datasets: AIMECORAL1 (580 images from the Southwest Indian Ocean) and AIMECORAL2 (282 images from New Caledonia, Pacific Ocean), encompassing diverse coral species and environmental conditions. The system's performance was evaluated using established metrics: object detection precision, Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), and Identity F1 Score (IDF1). Precision improved from 59.9 % (after fine-tuning on AIMECORAL1) to 84.7 % on the combined datasets. The tracking system achieved a MOTA of 82.63 %, MOTP of 83.28 %, and IDF1 of 70.76 %, demonstrating reliable multi-object tracking in complex underwater environments. We applied our framework to a case study involving video transects from an outer reef site in New Caledonia, comparing data from 2021 and 2022. This automated solution offers a scalable, cost-effective alternative to traditional monitoring methods, supporting seamless, large-scale reef assessment. By leveraging deep learning, our approach enables more efficient data collection, contributing to the protection of these vulnerable ecosystems in the face of increasing environmental pressures. |
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| ISSN: | 1574-9541 |