RipFinder: real-time rip current detection on mobile devices
Rip currents present a significant safety risk to beach tourists and coastal communities, resulting in hundreds of annual drownings all over the world. A key contributing factor to this danger is the lack of awareness among beachgoers about recognizing and avoiding these rip currents. In response to...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1549513/full |
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| author | Fahim Khan Akila de Silva Ashleigh Palinkas Gregory Dusek James Davis Alex Pang |
| author_facet | Fahim Khan Akila de Silva Ashleigh Palinkas Gregory Dusek James Davis Alex Pang |
| author_sort | Fahim Khan |
| collection | DOAJ |
| description | Rip currents present a significant safety risk to beach tourists and coastal communities, resulting in hundreds of annual drownings all over the world. A key contributing factor to this danger is the lack of awareness among beachgoers about recognizing and avoiding these rip currents. In response to this issue, we introduce RipFinder, a mobile app equipped with machine learning (ML) models trained to detect two types of rip currents. Users can leverage the app’s computer vision capabilities to use their phone’s camera to identify these hazardous rip currents in real time. The amorphous and ephemeral nature of rip currents makes it challenging to detect them with high accuracy using object detection models. To address this, we propose a client-server ML model-based computer vision system designed specifically to improve rip current detection accuracy. This novel approach enables the app to function with or without internet connectivity, proving particularly beneficial in regions without lifeguards or internet access. Additionally, the app serves as an educational resource, offering in-app information about rip currents. It also promotes citizen science involvement by encouraging users to contribute valuable information on detected rip currents. This paper presents the app’s overall design and discusses the challenges inherent to the rip current detection system. |
| format | Article |
| id | doaj-art-92bbf19cf2414c938b360470fa20841f |
| institution | OA Journals |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-92bbf19cf2414c938b360470fa20841f2025-08-20T02:33:12ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-05-011210.3389/fmars.2025.15495131549513RipFinder: real-time rip current detection on mobile devicesFahim Khan0Akila de Silva1Ashleigh Palinkas2Gregory Dusek3James Davis4Alex Pang5Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, CA, United StatesDepartment of Computer Science, San Francisco State University, San Francisco, CA, United StatesScripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United StatesNational Ocean Service, National Oceanic and Atmospheric Administration, Silver Spring, MD, United StatesDepartment of Computer Science and Engineering, University of California, Santa Cruz, Santa Cruz, CA, United StatesDepartment of Computer Science and Engineering, University of California, Santa Cruz, Santa Cruz, CA, United StatesRip currents present a significant safety risk to beach tourists and coastal communities, resulting in hundreds of annual drownings all over the world. A key contributing factor to this danger is the lack of awareness among beachgoers about recognizing and avoiding these rip currents. In response to this issue, we introduce RipFinder, a mobile app equipped with machine learning (ML) models trained to detect two types of rip currents. Users can leverage the app’s computer vision capabilities to use their phone’s camera to identify these hazardous rip currents in real time. The amorphous and ephemeral nature of rip currents makes it challenging to detect them with high accuracy using object detection models. To address this, we propose a client-server ML model-based computer vision system designed specifically to improve rip current detection accuracy. This novel approach enables the app to function with or without internet connectivity, proving particularly beneficial in regions without lifeguards or internet access. Additionally, the app serves as an educational resource, offering in-app information about rip currents. It also promotes citizen science involvement by encouraging users to contribute valuable information on detected rip currents. This paper presents the app’s overall design and discusses the challenges inherent to the rip current detection system.https://www.frontiersin.org/articles/10.3389/fmars.2025.1549513/fullrip current detectiondata collectioncitizen sciencecoastal observationcomputer visiondeep learning |
| spellingShingle | Fahim Khan Akila de Silva Ashleigh Palinkas Gregory Dusek James Davis Alex Pang RipFinder: real-time rip current detection on mobile devices Frontiers in Marine Science rip current detection data collection citizen science coastal observation computer vision deep learning |
| title | RipFinder: real-time rip current detection on mobile devices |
| title_full | RipFinder: real-time rip current detection on mobile devices |
| title_fullStr | RipFinder: real-time rip current detection on mobile devices |
| title_full_unstemmed | RipFinder: real-time rip current detection on mobile devices |
| title_short | RipFinder: real-time rip current detection on mobile devices |
| title_sort | ripfinder real time rip current detection on mobile devices |
| topic | rip current detection data collection citizen science coastal observation computer vision deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1549513/full |
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