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YFDM: YOLO for detecting Morse code.

Zhenhua Wei1, Zijun Li2, Siming Han1

  • 1Academy of Operational Support Rocket Force Engineering University, Xi'an, 710025, China.

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|November 23, 2023
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This summary is machine-generated.

This study introduces YFDM, an efficient Morse code detection algorithm. It significantly reduces model size and computational load compared to YOLOv5 while maintaining high accuracy and speed for complex communication environments.

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Manual Morse code detection struggles with complex shortwave communication environments.
  • Existing methods lack the efficiency and accuracy required for modern applications.

Purpose of the Study:

  • To develop an automated Morse code detection algorithm (YFDM) for enhanced efficiency and accuracy.
  • To improve upon existing object detection models for signal analysis.

Main Methods:

  • Utilized a backbone network with deformable convolution and C3 modules for enhanced feature extraction.
  • Implemented a lightweight neck network using GSConv and VOV-GSCSP modules.
  • Employed the confidence propagation cluster (CP-Cluster) algorithm for detection frame filtering.

Main Results:

  • YFDM demonstrated 15.11% fewer parameters and 38.9% fewer GFLOPs than YOLOv5.
  • Achieved a highest AP0.5:0.95 of 0.68 and 72.4 FPS when using WIoUv1 loss function.
  • The algorithm effectively reduces model weight while ensuring detection accuracy and inference speed.

Conclusions:

  • The YFDM algorithm offers a significant improvement in Morse code detection efficiency and accuracy.
  • It provides a lightweight and fast solution suitable for complex signal environments.
  • The proposed method balances model compression with high performance in signal detection.