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This study optimizes the YOLOv2 algorithm for low-power embedded chips, enabling an intelligent fan to detect people and adjust airflow. This AI-driven approach enhances user comfort and air delivery accuracy.

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Area of Science:

  • Artificial Intelligence
  • Embedded Systems
  • Computer Vision

Background:

  • The integration of artificial intelligence (AI) with low-power embedded chips is a growing research area.
  • Object detection algorithms are crucial for intelligent systems to perceive their environment.

Purpose of the Study:

  • To optimize the YOLOv2 algorithm for deployment on the K210 chip for face object detection.
  • To develop an intelligent fan system capable of detecting human presence and adjusting operational parameters.

Main Methods:

  • Tailored optimization of the YOLOv2 algorithm for the K210 embedded chip.
  • Training a dedicated face object detection model.
  • Implementing intelligent algorithms for fan control based on detected human position and size.

Main Results:

  • Successful deployment of the optimized YOLOv2 model on the K210 chip.
  • The intelligent fan accurately detects human targets and determines their position and size.
  • The system intelligently adjusts the fan's turning angle and air supply size.
  • Demonstrated effectiveness in solo and crowd tracking, and automatic air volume adjustment.

Conclusions:

  • The proposed method offers a novel approach to AI on embedded chips by optimizing YOLOv2.
  • The intelligent fan system improves air delivery accuracy and user comfort.
  • This research provides a strong foundation for AI and embedded systems integration in diverse applications.