Underwater Drowning Detection Dataset

<b>Underwater Drowning Detection Dataset</b><br>This dataset contains 5,613 manually annotated underwater images for drowning detection research, captured in controlled swimming pool environments. It provides a balanced distribution of three behavioral states:<br><b>Swimming</b> (1,871 images)<b>Struggling</b> (1,871 images)<b>Drowning</b> (1,871 images)All images were collected under real underwater conditions and annotated for object detection tasks using the YOLO format.<b>Key Features</b>High-resolution underwater images (640×640 pixels, RGB)YOLO <code>.txt</code> annotations with bounding boxes for three behavior classesBalanced class distribution to minimize model biasData collected ethically with lifeguard supervision and participant consentIncludes realistic challenges such as water distortion and lighting variability<b>Technical Details</b><b>Total Images:</b> 5,613<b>Training/Validation Split:</b> 4,488 / 1,125<b>Classes:</b> Swimming, Struggling, Drowning<b>Format:</b> JPEG + YOLO annotation files<b>Resolution:</b> 640×640 pixels<b>Baseline Performance:</b> YOLOv8n achieved 97.5% mAP@50 on this dataset<b>Annotation Format</b><br>Each image has a corresponding .txt file with annotations in YOLO format, where each line follows this structure: <br><br><b>Field Descriptions:</b><br><br>class_id: Integer label for the class<br>0 = Swimming<br>1 = Struggling<br>2 = Drowningx_center, y_center: Normalized center coordinates of the bounding box (values between 0.0 and 1.0)width, height: Normalized width and height of the bounding box (values between 0.0 and 1.0)<b>Example Annotation:</b><br><br>0 0.509896 0.568519 0.453125 0.581481This line indicates a “Swimming” detection (class_id = 0) with a bounding box centered at 50.99% (horizontal) and 56.85% (vertical) of the image dimensions, covering 45.31% of the width and 58.15% of the height.<b>Dataset Folder Structure</b><br>datasets/<br>├── images/<br>│ ├── train/<br>│ │ ├── frame_00001.jpg<br>│ │ └── ...<br>│ └── val/<br>│ ├── frame_04489.jpg<br>│ └── ...<br>│<br>├── labels/<br>│ ├── train/<br>│ │ ├── frame_00001.txt<br>│ │ └── ...<br>│ └── val/<br>│ ├── frame_04489.txt<br>│ └── ...<br>│<br>├── classes.txt<br>├── README.md<br><b>Use and Applications</b><br>This dataset is designed to support the development and evaluation of real-time AI systems for aquatic safety, including:<br>Drowning detection modelsMulti-class object detection in underwater environmentsResearch in underwater computer vision and human activity recognition<b>Citation</b><br>If you use this dataset, please cite:<br><pre>graphqlCopyEdit<pre>@dataset{underwater_drowning_detection_2025,<br> title = {Underwater Drowning Detection Dataset},<br> author = {Hamad Alzaabi and Saif Alzaabi and Sarah Kohail},<br> year = {2025},<br> publisher = {Figshare},<br> note = {Manually annotated underwater images for drowning detection research}<br>}<br></pre></pre>Please also cite the related publication:<br><br>mathematicaCopyEdit<pre>@inproceedings{Alzaabi2025,<br> author = {Hamad Alzaabi and Saif Alzaabi and Sarah Kohail},<br> title = {Multi‑Swimmer Drowning Detection Using a Custom Annotated Underwater Dataset and Real‑Time AI},<br> booktitle = {Proceedings of the International Conference on Image Analysis and Processing (ICIAP)},<br> year = {2025}<br>}</pre><br>

搜集汇总

背景与挑战

背景概述

该数据集是一个专门用于溺水检测研究的水下图像数据集，包含5,613张手动标注的图像，均匀覆盖游泳、挣扎和溺水三种行为状态，图像分辨率为640×640像素，并采用YOLO格式进行标注。数据集在受控游泳池环境中采集，具有水失真和光照变化等现实挑战，已划分为训练集和验证集，支持开发实时AI系统以提升水上安全。