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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
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In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
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Optical Fiber Vibration Signal Identification Method Based on Improved YOLOv4.

Jiangwei Zhang1, Jiaqing Mo1, Xinrong Ma1

  • 1Xinjiang Key Laboratory of Signal Detection and Processing, School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China.

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|December 11, 2022
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Summary

This study enhances optical fiber vibration signal recognition by integrating endpoint detection and pattern recognition using an improved You Only Look Once v4 (YOLOv4) model. The novel approach significantly boosts detection accuracy and speed for early-warning systems.

Keywords:
YOLOv4deep separable convolutional networkdistributed optical fiber vibration sensorfocal loss functionobject detection

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Area of Science:

  • Sensor Technology
  • Signal Processing
  • Machine Learning

Background:

  • Traditional early-warning systems process optical fiber vibration signals sequentially.
  • Integrating endpoint detection and pattern recognition is crucial for improving efficiency.
  • Current methods face challenges in real-time processing and accuracy.

Purpose of the Study:

  • To develop an improved You Only Look Once v4 (YOLOv4) model for simultaneous endpoint detection and pattern recognition of optical fiber vibration signals.
  • To enhance the speed and accuracy of vibration signal identification in security systems.
  • To address the limitations of existing target detection algorithms in terms of complexity and dataset imbalance.

Main Methods:

  • Implemented a deep separable convolution (DSC) network to reduce YOLOv4 model complexity.
  • Utilized a focal loss function to address unbalanced sample classification.
  • Visualized real-time collected signals as oscillograph and time-frequency datasets for detection.
  • Combined endpoint detection and pattern recognition into a unified target detection algorithm.

Main Results:

  • Achieved high mean Average Precision (mAP) of 98.50% and 93.48% on different datasets.
  • Reached impressive frames per second (FPS) of 84.8 and 69.9, indicating significant speed improvements.
  • The improved algorithm demonstrated a detection speed approximately 20 times faster than other optical fiber vibration signal recognition algorithms.
  • Outperformed the standard YOLOv4 model in both mAP and FPS metrics.

Conclusions:

  • The enhanced YOLOv4 algorithm effectively integrates endpoint detection and pattern recognition for optical fiber vibration signals.
  • The proposed method significantly improves detection accuracy and real-time processing speed for early-warning systems.
  • This approach reduces false alarms and enhances the overall performance of security systems by enabling rapid and accurate identification of external intrusions.