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Deep learning models compared for underwater kelp segmentation · 1 source tracked

By PulseAugur Editorial · [1 sources] ·

Researchers have evaluated several deep learning architectures for segmenting kelp forests in underwater imagery. The study focused on three frameworks: ResNet34-U-Net, ResNet50-DeepLabV3, and a hybrid ResNet50-ASPP-Transformer. Using a dataset of 3,395 annotated images from the U.S. coast, the ResNet50-DeepLabV3 model, named Kelp-o-Tron, achieved the highest accuracy metrics, including Dice and Intersection over Union (IoU). This model demonstrated superior consistency and generalization across various environmental conditions, making it a promising tool for automated underwater habitat mapping. AI

IMPACT This research offers improved methods for automated underwater habitat mapping and ecological monitoring through advanced deep learning segmentation.

RANK_REASON Academic paper presenting a comparative assessment of deep learning architectures for a specific segmentation task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning models compared for underwater kelp segmentation · 1 source tracked

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Academic paper presenting a comparative assessment of deep learning architectures for a specific segmentation task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Sundarabalan Balasubramanian, C\'esar Borja, Ana C. Murillo, Lexi N. Wilkes, Meredith L. McPherson, Kira A. Krumhansl, Jennifer A. Dijkstra, Jarrett E. K. Byrnes ·

    Comparative Assessment of Deep Learning Architectures for Underwater Subsurface Kelp Forest Segmentation with The Kelp-o-Tron

    arXiv:2608.24594v1 Announce Type: new Abstract: Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation remains challenging due to optical degradation, illumination variability, turbidi…

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