YOLO
YOLO on World Data Ocean: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on yolo in some way — the news, the analysis, the deep dives, and the occasional surprise find. A Global Hub for Ocean Intelligence 4 World Data Ocean is a centralized digital platform where researchers, scientists, and ocean enthusiasts converge to explore, analyze, and… New stories are added to this page as we find them, so check back if you want to keep up with what is happening around yolo, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything World Data Ocean is covering right now.
Using semi-supervised learning to detect beluga whales from aerial image sequences
Precise beluga whale population monitoring is essential for Arctic conservation, yet manual annotation of aerial imagery presents significant logistical and cost barriers. This study investigates a solution: semi-supervised learning. We systematically evaluated SEMI-DETR, a novel detection transformer, against supervised methods, demonstrating a 20% mean Average Precision improvement with just 1% of labeled data. Notably, calf detection—critical for assessing reproductive health—benefited most. These findings establish an empirical benchmark and offer practical guidance for resource-constrained conservation programs, mirroring approaches explored in our related work on plankton monitoring.
Improved Transformer-based detection of underwater plastic debris in complex environments
Accurate detection of underwater plastic debris presents a significant challenge due to image degradation, small debris size, and complex backgrounds. This study introduces an improved RF-DETR detector, leveraging frequency-aware feature reweighting and adaptive query strategies to enhance the representation of challenging features. Evaluations on the TrashCan and DeepTrash datasets demonstrate superior performance compared to established baselines, achieving notable precision and mAP scores.
Machine learning, eDNA and citizen science in monitoring and assessing biodiversity and invasive alien species at sea
Emerging technologies are revolutionizing marine biodiversity and invasive alien species monitoring. This research, presented at a recent summer school, details the integration of machine learning, citizen science, and environmental DNA (eDNA) alongside remote sensing and drones. Machine learning, utilizing frameworks like Essential Biodiversity Variables (EBVs), enables efficient species identification and global data synthesis. Initiatives like iNaturalist exemplify the power of citizen science combined with AI validation.
PICOGRAM - informing coral reef resilience-based management through prediction of individual coral organismal growth, recruitment, and mortality
PICOGRAM introduces a novel, open-source framework for scalable coral reef monitoring, addressing the limitations of manual annotation in image-based assessments. Inspired by the NASA NeMO-Net ecosystem, PICOGRAM leverages a Segment Anything Model (SAM) to automate coral colony detection and percent-cover estimation from underwater imagery and orthomosaics. Evaluated against expert annotations, PICOGRAM achieves high accuracy (IoU 87.5% in-domain) and calibrated quality scores, enabling efficient refinement with minimal user input.