DOI: 10.6084/m9.figshare.29497235.v2
title: Underwater Drowning Detection Dataset

description: <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>