BenthIQ: a Transformer-Based Benthic Classification Model for Coral Restoration
This work addresses the need for improved benthic classification to support coral restoration and management, offering a domain-specific incremental advancement over existing methods.
The paper tackles the problem of monitoring coral reef health by developing BenthIQ, a transformer-based model for high-precision classification of underwater substrates like live coral and algae, demonstrating that it outperforms traditional CNN and attention-based models on pixel-wise classification in a real-world case study in French Polynesia.
Coral reefs are vital for marine biodiversity, coastal protection, and supporting human livelihoods globally. However, they are increasingly threatened by mass bleaching events, pollution, and unsustainable practices with the advent of climate change. Monitoring the health of these ecosystems is crucial for effective restoration and management. Current methods for creating benthic composition maps often compromise between spatial coverage and resolution. In this paper, we introduce BenthIQ, a multi-label semantic segmentation network designed for high-precision classification of underwater substrates, including live coral, algae, rock, and sand. Although commonly deployed CNNs are limited in learning long-range semantic information, transformer-based models have recently achieved state-of-the-art performance in vision tasks such as object detection and image classification. We integrate the hierarchical Swin Transformer as the backbone of a U-shaped encoder-decoder architecture for local-global semantic feature learning. Using a real-world case study in French Polynesia, we demonstrate that our approach outperforms traditional CNN and attention-based models on pixel-wise classification of shallow reef imagery.