SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild
Jannik Els\"a{\ss}er, Laura Weihl, Veronika Cheplygina, Lisbeth Tangaa Nielsen

TL;DR
This paper presents SeagrassFinder, a deep learning approach using models like Vision Transformers to automate seagrass detection and coverage estimation from underwater videos, significantly improving efficiency and accuracy for marine ecosystem monitoring.
Contribution
The work introduces a new annotated dataset, evaluates multiple deep learning architectures, and proposes a novel method for estimating seagrass coverage from underwater videos.
Findings
Deep learning models achieve AUROC > 0.95 in seagrass presence detection.
Underwater image enhancement improves model performance.
Preliminary results show promising coverage estimation aligned with expert labels.
Abstract
Seagrass meadows play a crucial role in marine ecosystems, providing benefits such as carbon sequestration, water quality improvement, and habitat provision. Monitoring the distribution and abundance of seagrass is essential for environmental impact assessments and conservation efforts. However, the current manual methods of analyzing underwater video data to assess seagrass coverage are time-consuming and subjective. This work explores the use of deep learning models to automate the process of seagrass detection and coverage estimation from underwater video data. We create a new dataset of over 8,300 annotated underwater images, and subsequently evaluate several deep learning architectures, including ResNet, InceptionNetV3, DenseNet, and Vision Transformer for the task of binary classification on the presence and absence of seagrass by transfer learning. The results demonstrate that…
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Taxonomy
TopicsMarine and coastal plant biology
MethodsAttention Is All You Need · ALIGN · *Communicated@Fast*How Do I Communicate to Expedia? · Batch Normalization · 1x1 Convolution · Concatenated Skip Connection · Global Average Pooling · Dense Block · Dilated Causal Convolution · Linear Layer
