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arxiv: 2604.17070 · v2 · submitted 2026-04-18 · 💻 cs.CV

Recognition: unknown

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

Aakash Ralhan, Abdullah Naeem, Akbarali Vakhitov, Amitabh Tripathi, Anav Katwal, Andrei Dumitriu, Anjana Nanditha, Asuka Shin, Ayon Dey, Chun'an Yu, Florin Miron, Florin Tatui, Gaurav Mahesh, Gejalakshmi N, Gundluri Yuvateja Reddy, Guoyi Xu, Harshitha Palaram, Hiroto Shirono, Jeevitha S, Jiachen Tu, Jiajia Liu, Jiji CV, Junhao Chen, Kosuke Shigematsu, Md Tamjidul Hoque, Modugumudi Mahesh, Radu Timofte, Radu Tudor Ionescu, Sang-Chul Lee, Santosh Kumar Vipparthi, Subrahmanyam Murala, Xinger Li, Yang Yang, Yaokun Shi, Yaoxin Jiang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 06:28 UTC · model grok-4.3

classification 💻 cs.CV
keywords rip current detectionimage segmentationNTIRE challengecomputer visionRipVIS benchmarkpretrained modelsbeach safetyhazard detection
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The pith

Pretrained general vision models achieve strong performance on rip current detection and segmentation across diverse beaches.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents the NTIRE 2026 Rip Current Detection and Segmentation Challenge, which tests automatic identification of hazardous rip currents in images from varied global locations and conditions. It introduces a diverse dataset sourced from over ten countries with multiple camera orientations and sea states, then evaluates nine valid submissions that mostly apply pretrained vision models plus augmentation and post-processing. The outcomes indicate that advances in general-purpose models transfer effectively to this safety-critical task. A reader would care because rip currents cause numerous beach fatalities each year, and reliable automated detection could support real-time warning systems.

Core claim

The challenge results on the RipVIS benchmark show that participant solutions relying on robust pretrained models, combined with strong augmentation and post-processing, produce competitive composite scores on detection and segmentation, suggesting that rip current understanding benefits strongly from the progress in general-purpose vision models while leaving ample room for future methods tailored to their unique visual structure.

What carries the argument

The RipVIS benchmark dataset paired with a composite ranking score that combines F1 and F2 metrics at IoU thresholds of 50 and 40:95 to evaluate both detection and segmentation tasks.

If this is right

  • Pretrained models with augmentation and post-processing form an effective baseline for rip current tasks.
  • General-purpose vision progress directly aids safety applications involving variable nearshore flows.
  • The benchmark dataset supports standardized future comparisons in this domain.
  • Tailored methods focused on rip-specific visual cues could close remaining performance gaps.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Existing vision systems could be integrated into beach monitoring cameras to provide alerts without requiring entirely new model development.
  • The dataset's multi-country coverage suggests models that generalize across viewpoints may scale to global safety tools.
  • Extending evaluation to video inputs would test whether the same approaches maintain consistency over time.

Load-bearing premise

The composite evaluation score combining F1 and F2 at different IoU thresholds accurately reflects practical performance for rip current detection in real-world deployment scenarios.

What would settle it

A controlled deployment test of the top three submitted models on a new beach location and sea state outside the dataset, measuring their precision against expert human annotations under live conditions.

Figures

Figures reproduced from arXiv: 2604.17070 by Aakash Ralhan, Abdullah Naeem, Akbarali Vakhitov, Amitabh Tripathi, Anav Katwal, Andrei Dumitriu, Anjana Nanditha, Asuka Shin, Ayon Dey, Chun'an Yu, Florin Miron, Florin Tatui, Gaurav Mahesh, Gejalakshmi N, Gundluri Yuvateja Reddy, Guoyi Xu, Harshitha Palaram, Hiroto Shirono, Jeevitha S, Jiachen Tu, Jiajia Liu, Jiji CV, Junhao Chen, Kosuke Shigematsu, Md Tamjidul Hoque, Modugumudi Mahesh, Radu Timofte, Radu Tudor Ionescu, Sang-Chul Lee, Santosh Kumar Vipparthi, Subrahmanyam Murala, Xinger Li, Yang Yang, Yaokun Shi, Yaoxin Jiang.

Figure 1
Figure 1. Figure 1: Examples from the RipVIS dataset [23], which also forms the basis of the RipDetSeg Challenge. The four columns illustrate different camera orientations: (a) aerial bird’s-eye, (b) aerial tilted, (c) elevated beachfront, and (d) water-level beachfront. The examples highlight the diversity of rip currents across locations, types, and viewpoints. Rip currents are visible through disrupted wave-breaking patter… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of Team SiGMoid’s pipeline. A YOLO11m [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of Team Riposte’s pipeline. Training only [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Team Soloseg’s schematic illustration of the standard [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Team KMG’s two stage pipeline using YOLOv13 and [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Team RIP YuvatejaReddy system pipeline.Images are processed by the YOLOv8s-seg, outputting both bounding box co￾ordinates and corresponding polygons. per image. These predictions are post-processed using IoU- and IoA-based merging to combine highly overlapping boxes and suppress redundant detections. In parallel, a DI￾NOv3 detector implemented in MMDetection is applied at high resolution and its output is … view at source ↗
Figure 8
Figure 8. Figure 8: Team VisionX’s mask-centric pipeline. Multi-scale in [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Team NTR’s inference pipeline. YOLO11x detection [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Overview of Team Amitabh’s YOLO11s-seg pipeline. [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
read the original 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-related fatalities worldwide, yet remain difficult to identify because their visual appearance varies substantially across beaches, viewpoints, and sea states. To advance research on this safety-critical problem, the challenge builds on the RipVIS benchmark, evaluating both detection and segmentation. The dataset is diverse, sourced from more than $10$ countries, with $4$ camera orientations and diverse beach and sea conditions. This report describes the dataset, challenge protocol, evaluation methodology, final results, and summarizes the main insights from the submitted methods. The challenge attracted $159$ registered participants and produced $9$ valid test submissions across the two tasks. Final rankings are based on a composite score that combines $F_1[50]$, $F_2[50]$, $F_1[40\!:\!95]$, and $F_2[40\!:\!95]$. Most participant solutions relied on pretrained models, combined with strong augmentation and post-processing design. These results suggest that rip current understanding benefits strongly from the robust general-purpose vision models' progress, while leaving ample room for future methods tailored to their unique visual structure.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The manuscript reports on the NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge. It describes the RipVIS benchmark dataset (diverse images from >10 countries, 4 camera orientations, varied beach/sea conditions), the two tasks (detection and segmentation), the evaluation protocol based on a composite score of F1[50], F2[50], F1[40:95] and F2[40:95], participation statistics (159 registered, 9 valid test submissions), the final rankings, and the main insights that most submissions used pretrained general-purpose vision models plus augmentation and post-processing, suggesting that rip current understanding benefits from general vision model progress while leaving room for methods tailored to rip currents' unique visual structure.

Significance. If the reported participation, rankings, and method summaries hold, the report is significant for establishing a standardized, diverse benchmark on a safety-critical task with direct potential to reduce beach fatalities. It provides a clear baseline showing transferability of recent general-purpose CV advances (via pretrained models) to this domain and identifies open challenges for specialized techniques. The factual, descriptive nature of the report, with no unsubstantiated causal claims, makes it a useful community resource for tracking progress on rip current detection and segmentation.

major comments (1)
  1. [Evaluation protocol] Evaluation protocol section: the composite score (F1[50] + F2[50] + F1[40:95] + F2[40:95]) is used to produce final rankings and underpins the interpretation of which methods succeed, yet the manuscript provides no justification, weighting rationale, or correlation analysis showing that this metric accurately reflects practical real-world performance for rip current detection in deployment scenarios.
minor comments (3)
  1. [Abstract and Dataset] Abstract and dataset description: the claim of sourcing from 'more than 10 countries' should be accompanied by the exact count and per-country distribution in the main text to support reproducibility and diversity claims.
  2. [Results] Results section: while the report notes that most solutions rely on pretrained models with augmentation and post-processing, a table or summary quantifying the performance gap versus non-pretrained baselines would strengthen the observational insight about benefits from general vision progress.
  3. [Participation] Participation details: the drop from 159 registered participants to 9 valid submissions is reported but not discussed; a short note on common failure modes or submission issues would aid future challenge organizers.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the evaluation protocol. We agree that additional justification is warranted and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: Evaluation protocol section: the composite score (F1[50] + F2[50] + F1[40:95] + F2[40:95]) is used to produce final rankings and underpins the interpretation of which methods succeed, yet the manuscript provides no justification, weighting rationale, or correlation analysis showing that this metric accurately reflects practical real-world performance for rip current detection in deployment scenarios.

    Authors: We acknowledge the need for explicit justification. The composite score was chosen to balance precision-recall trade-offs (via F1 and F2) while incorporating both a standard IoU=0.5 threshold and the COCO-style averaged [0.4:0.95] range for robustness to localization quality. F2 weighting prioritizes recall, which aligns with the safety-critical nature of rip current detection where missing a hazard is far costlier than false alarms. This follows conventions from established benchmarks such as COCO and Pascal VOC. A direct correlation study with real-world deployment performance is not feasible here, as it would require operational data from beach safety systems that is unavailable to the challenge organizers. In the revision we will add a concise rationale paragraph in the Evaluation Protocol section, including the safety-motivated weighting and an explicit statement of this limitation. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a descriptive challenge report summarizing dataset construction, evaluation protocol, participant submissions, and observed performance trends. It contains no mathematical derivations, fitted parameters, predictions, or load-bearing self-citations. All statements follow directly from reported external submissions and standard metrics without any reduction to internal definitions or ansatzes.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are involved as this is a descriptive challenge report without theoretical derivations or modeling.

pith-pipeline@v0.9.0 · 5710 in / 1100 out tokens · 51500 ms · 2026-05-10T06:28:49.153979+00:00 · methodology

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