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:<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>Dataset Folder Structure</b>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: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>

**水下溺水检测数据集**

本数据集包含5613张经人工标注的水下图像，用于溺水检测研究，所有图像均采集于受控游泳池环境中。该数据集涵盖三类行为状态且分布均衡：游泳（Swimming）类共1871张、挣扎（Struggling）类共1871张、溺水（Drowning）类共1871张。所有图像均采集自真实水下场景，并采用YOLO格式完成目标检测任务的标注。

## 核心特性
1. 高清水下图像：分辨率为640×640像素，采用RGB色彩空间；
2. 标注格式：针对三类行为类别生成带有边界框的YOLO格式`.txt`标注文件；
3. 类别均衡：类别分布均衡，可有效降低模型训练偏差；
4. 伦理采集：数据采集遵循伦理规范，全程由救生员监督并获得参与者知情同意；
5. 真实挑战：涵盖水下水体畸变、光照变化等真实场景挑战。

## 技术细节
- 图像总量：5613张
- 训练集/验证集划分：4488张 / 1125张
- 类别：游泳（Swimming）、挣扎（Struggling）、溺水（Drowning）
- 文件格式：JPEG图像 + YOLO标注文件
- 图像分辨率：640×640像素
- 基准性能：YOLOv8n在本数据集上取得了97.5%的mAP@50指标

## 数据集文件夹结构

datasets/
├── images/
│ ├── train/
│ │ ├── frame_00001.jpg
│ │ └── ...
│ └── val/
│ ├── frame_04489.jpg
│ └── ...
│
├── labels/
│ ├── train/
│ │ ├── frame_00001.txt
│ │ └── ...
│ └── val/
│ ├── frame_04489.txt
│ └── ...
│
├── classes.txt
├── README.md

## 使用场景与应用方向
本数据集旨在支持水下安全领域实时AI系统的开发与评估，具体应用包括：
1. 溺水检测模型开发；
2. 水下环境多类别目标检测研究；
3. 水下计算机视觉与人类行为识别相关研究。

## 引用规范
若您使用本数据集，请引用如下文献：
graphqlCopyEdit
@dataset{underwater_drowning_detection_2025,
title = {Underwater Drowning Detection Dataset},
author = {Hamad Alzaabi and Saif Alzaabi and Sarah Kohail},
year = {2025},
publisher = {Figshare},
note = {Manually annotated underwater images for drowning detection research}
}

同时请引用相关研究论文：
mathematicaCopyEdit
@inproceedings{Alzaabi2025,
author = {Hamad Alzaabi and Saif Alzaabi and Sarah Kohail},
title = {Multi‑Swimmer Drowning Detection Using a Custom Annotated Underwater Dataset and Real‑Time AI},
booktitle = {Proceedings of the International Conference on Image Analysis and Processing (ICIAP)},
year = {2025}
}