Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results

2023-06-01

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

AI Summary

This paper addresses the challenging problem of automated rip current detection in beach environments. We formally introduce the task of instance-level rip current segmentation—a novel formulation for fine-grained, pixel-accurate identification. To support this task, we construct the first high-quality multimodal benchmark dataset, comprising 2,466 images with polygon annotations and 17 drone-captured video sequences (24K frames) with frame-level mask annotations. Methodologically, we adapt the YOLOv8 instance segmentation architecture into a lightweight, real-time framework capable of processing both static images and video streams. Our optimized YOLOv8-nano variant achieves 88.94% mAP₅₀ on the validation set and 81.21% macro-average precision on test videos. All code, pre-trained models, and annotated data are publicly released. This work establishes a deployable baseline—suitable for edge devices—for intelligent, real-time rip current monitoring, thereby advancing both benchmarking and practical deployment in coastal safety applications.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water currents. In this paper, we address a novel task: rip current instance segmentation. We introduce a comprehensive dataset containing 2,466 images with newly created polygonal annotations for instance segmentation, used for training and validation. Additionally, we present a novel dataset comprising 17 drone videos (comprising about 24K frames) captured at 30 FPS, annotated with both polygons for instance segmentation and bounding boxes for object detection, employed for testing purposes. We train various versions of YOLOv8 for instance segmentation on static images and assess their performance on the test dataset (videos). The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of 88.94% on the validation dataset and 81.21% macro average on the test dataset. The results provide a baseline for future research in rip current segmentation. Our work contributes to the existing literature by introducing a detailed, annotated dataset, and training a deep learning model for instance segmentation of rip currents. The code, training details and the annotated dataset are made publicly available at https://github.com/Irikos/rip_currents.

Problem

• Automatically detect hazardous rip currents for beach safety
• Develop instance segmentation model using YOLOv8 for rip currents
• Create annotated datasets for training and testing rip current detection

Innovation

• YOLOv8 for rip current instance segmentation
• Comprehensive dataset with polygonal annotations
• Drone video dataset for testing performance