The AIM 2025 RipSeg Challenge report presents results from five submissions on single-class instance segmentation of rip currents, highlighting deep learning and domain adaptation techniques on a diverse beach dataset.
Ripnet: A lightweight one-class deep neural network for the identification of rip currents
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The NTIRE 2026 RipDetSeg Challenge evaluated AI methods for rip current detection and segmentation, finding that pretrained general-purpose models with augmentation and post-processing performed well on a diverse multi-country dataset.
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AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report
The AIM 2025 RipSeg Challenge report presents results from five submissions on single-class instance segmentation of rip currents, highlighting deep learning and domain adaptation techniques on a diverse beach dataset.
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NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report
The NTIRE 2026 RipDetSeg Challenge evaluated AI methods for rip current detection and segmentation, finding that pretrained general-purpose models with augmentation and post-processing performed well on a diverse multi-country dataset.