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A lightweight YOLOv8n-based method for human abnormal posture detection.

Guilin Li1, Jiarui Zhang2, Qiyuan Ji1

  • 1School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.

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Summary
This summary is machine-generated.

This study introduces PSD-YOLOv8n, a lightweight algorithm for detecting abnormal human postures, improving accuracy and efficiency in healthcare and safety applications. The model achieves high performance with a compact size, offering a reliable solution for real-time monitoring.

Keywords:
Attention mechanismHuman abnormal posture detectionLightweightYOLOv8n

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Abnormal human posture detection is crucial for smart healthcare, public safety, and elderly care.
  • Existing methods face challenges like occlusion, pose variation, data imbalance, and large model sizes, hindering real-time performance.

Purpose of the Study:

  • To develop a lightweight and accurate human abnormal posture detection algorithm.
  • To address limitations of current methods in complex scenarios and resource-constrained environments.

Main Methods:

  • Proposed PSD-YOLOv8n algorithm based on YOLOv8n, incorporating a PoseMSA module with spatial and channel attention.
  • Utilized separable convolutions and residual bottlenecks for efficient feature extraction.
  • Introduced a keypoint-aware KA-Sample upsampling module and a Detect-PSA detection head for enhanced spatial relationship modeling.

Main Results:

  • PSD-YOLOv8n achieved 97.8% mAP@0.5 and 75.8% mAP@0.5:0.95 on the SSHDataset.
  • Outperformed baseline YOLOv8n and advanced models like YOLOv9-T, YOLOv10n, and YOLOv11n.
  • Model boasts a compact 2.07M parameter count and 4.5MB weight size, demonstrating lightweight, high-precision capabilities.

Conclusions:

  • PSD-YOLOv8n offers an efficient and reliable solution for abnormal human posture detection.
  • The algorithm shows robustness in complex environments and diverse postures.
  • The lightweight design makes it suitable for real-time applications in smart healthcare and safety monitoring.