NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report
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| Format: | Preprint |
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2026
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| _version_ | 1866917443960569856 |
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| author | Dumitriu, Andrei Ralhan, Aakash Miron, Florin Tatui, Florin Ionescu, Radu Tudor Timofte, Radu Naeem, Abdullah Katwal, Anav Dey, Ayon Hoque, Md Tamjidul Shin, Asuka Shirono, Hiroto Shigematsu, Kosuke Mahesh, Gaurav Nanditha, Anjana CV, Jiji Vakhitov, Akbarali Lee, Sang-Chul Li, Xinger Yu, Chun'an Chen, Junhao Yang, Yang Reddy, Gundluri Yuvateja Palaram, Harshitha N, Gejalakshmi S, Jeevitha Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun Tripathi, Amitabh Mahesh, Modugumudi Vipparthi, Santosh Kumar Murala, Subrahmanyam |
| author_facet | Dumitriu, Andrei Ralhan, Aakash Miron, Florin Tatui, Florin Ionescu, Radu Tudor Timofte, Radu Naeem, Abdullah Katwal, Anav Dey, Ayon Hoque, Md Tamjidul Shin, Asuka Shirono, Hiroto Shigematsu, Kosuke Mahesh, Gaurav Nanditha, Anjana CV, Jiji Vakhitov, Akbarali Lee, Sang-Chul Li, Xinger Yu, Chun'an Chen, Junhao Yang, Yang Reddy, Gundluri Yuvateja Palaram, Harshitha N, Gejalakshmi S, Jeevitha Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun Tripathi, Amitabh Mahesh, Modugumudi Vipparthi, Santosh Kumar Murala, Subrahmanyam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17070 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report Dumitriu, Andrei Ralhan, Aakash Miron, Florin Tatui, Florin Ionescu, Radu Tudor Timofte, Radu Naeem, Abdullah Katwal, Anav Dey, Ayon Hoque, Md Tamjidul Shin, Asuka Shirono, Hiroto Shigematsu, Kosuke Mahesh, Gaurav Nanditha, Anjana CV, Jiji Vakhitov, Akbarali Lee, Sang-Chul Li, Xinger Yu, Chun'an Chen, Junhao Yang, Yang Reddy, Gundluri Yuvateja Palaram, Harshitha N, Gejalakshmi S, Jeevitha Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun Tripathi, Amitabh Mahesh, Modugumudi Vipparthi, Santosh Kumar Murala, Subrahmanyam Computer Vision and Pattern Recognition cs.AI I.4.0; I.4.9 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. |
| title | NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report |
| topic | Computer Vision and Pattern Recognition cs.AI I.4.0; I.4.9 |
| url | https://arxiv.org/abs/2604.17070 |