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NTIRE 2026 challenge tackles rip current detection and segmentation

By PulseAugur Editorial · [1 sources] ·

The NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge focused on developing AI systems to automatically identify hazardous rip currents in images. This safety-critical problem is challenging due to the visual variability of rip currents across different environments and conditions. The challenge utilized a diverse dataset from over 10 countries and attracted 159 registered participants, with 9 valid submissions for detection and segmentation tasks. Most successful methods leveraged pretrained vision models, indicating the benefit of general-purpose AI advancements while highlighting opportunities for specialized approaches. AI

IMPACT Advances in rip current detection could improve beach safety and reduce drowning incidents through AI-powered monitoring.

RANK_REASON This is a research paper reporting on a challenge and dataset for computer vision tasks.

Read on arXiv cs.CV →

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

NTIRE 2026 challenge tackles rip current detection and segmentation

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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Andrei Dumitriu, Aakash Ralhan, Florin Miron, Florin Tatui, Radu Tudor Ionescu, Radu Timofte, Abdullah Naeem, Anav Katwal, Ayon Dey, Md Tamjidul Hoque, Asuka Shin, Hiroto Shirono, Kosuke Shigematsu, Gaurav Mahesh, Anjana Nanditha, Jiji CV, Akbarali Vakhit ·

    NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report

    arXiv:2604.17070v2 Announce Type: replace Abstract: This report presents the NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge, which targets automatic rip current understanding in images. Rip currents are hazardous nearshore flows that cause many beach-rela…

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