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MS-YOLO: A Lightweight and High-Precision YOLO Model for Drowning Detection.

Qi Song1,2, Bodan Yao1, Yunlong Xue1

  • 1School of Automation, Shenyang Aerospace University, Shenyang 110136, China.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
Summary

A new drowning detection model, MS-YOLO, offers high precision and efficiency for rescue operations. This lightweight model enhances small object detection and feature identification in aquatic environments.

Keywords:
MS-YOLOYOLOv8drowning detectionlightweight high-accuracy model

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Marine Safety

Background:

  • Drowning remains a significant cause of accidental death globally.
  • Existing detection systems often lack the precision and efficiency required for real-time rescue operations.
  • Intelligent hardware platforms require lightweight and accurate detection models.

Purpose of the Study:

  • To develop a novel, lightweight, and high-precision detection model named MS-YOLO.
  • To enhance the efficiency of drowning rescue operations through improved object detection.
  • To ensure the model's applicability on intelligent hardware platforms.

Main Methods:

  • Introduced the MD-C2F structure with dynamic convolution (DcConv) for capturing subtle aquatic movements.
  • Incorporated the EMA mechanism for improved small object detection within the MD-C2F structure.
  • Developed the MSI-SPPF module for multi-scale feature identification and complex background understanding.
  • Replaced ConCat fusion with BiFPN weighted channel fusion for retaining relevant drowning features.

Main Results:

  • Achieved an average detection accuracy of 86.4% on a self-built dataset.
  • Operates at an ultra-low computational cost of 7.3 GFLOPs.
  • Demonstrated superior performance compared to Faster R-CNN, SSD, YOLOv6, YOLOv9, and YOLOv10.

Conclusions:

  • MS-YOLO significantly improves drowning detection efficiency and accuracy.
  • The model's lightweight design and low computational cost make it suitable for intelligent hardware.
  • This advancement holds potential for enhancing water safety and reducing drowning incidents.