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A Pool Drowning Detection Model Based on Improved YOLO.

Wenhui Zhang1, Lu Chen1, Jianchun Shi2

  • 1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

A new YOLO11-LiB model enhances drowning detection in swimming pools. This efficient AI system offers high accuracy for real-time safety monitoring.

Keywords:
YOLO11attentiondrowning detectionfeature fusionlightweight

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Health

Background:

  • Drowning is a leading cause of adolescent fatalities.
  • Current surveillance methods in pools have limitations.
  • Existing AI models struggle with efficiency and complex conditions.

Purpose of the Study:

  • To develop an efficient and robust AI model for drowning detection.
  • To improve real-time safety monitoring in swimming pool environments.
  • To address limitations of current vision models in edge deployment.

Main Methods:

  • Proposed YOLO11-LiB model based on YOLO11n.
  • Introduced Lightweight Feature Extraction Module (LGCBlock) with Ghost Convolution and dynamic convolution.
  • Integrated Cross-Channel Position-aware Spatial Attention (C2PSAiSCSA) and Bidirectional Feature Fusion Network (BiFF-Net).

Main Results:

  • YOLO11-LiB achieved 94.1% drowning class mean average precision (DmAP50).
  • The model has only 2.02 million parameters and a size of 4.25 MB.
  • Demonstrated a balance between accuracy and efficiency.

Conclusions:

  • YOLO11-LiB offers a high-performance solution for real-time drowning detection.
  • The model is suitable for edge deployment in swimming pools.
  • This research contributes to improving aquatic safety through advanced AI.