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Flying Insect Detection and Classification with Inexpensive Sensors

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Few-Shot Classification with Meta-Learning for Urban Infrastructure Monitoring Using Distributed Acoustic Sensing.

Huynh Van Luong¹, Nikos Deligiannis^2,3, Roman Wilhelm¹

¹AP Sensing GmbH, Herrenberger Str. 130, 71034 Böblingen, Germany.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

This summary is machine-generated.

This study introduces a few-shot classification framework using meta-learning for distributed acoustic sensing (DAS) data. The method efficiently classifies urban infrastructure events with limited data, offering flexible pre-processing options.

Keywords:

artificial intelligence distributed acoustic sensing few-shot classification meta-learning neural networks

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Area of Science:

Machine Learning
Signal Processing
Urban Infrastructure Monitoring

Background:

Distributed Acoustic Sensing (DAS) is crucial for monitoring urban infrastructure.
Few-shot classification is needed for event detection with limited labeled data.

Purpose of the Study:

To develop and evaluate a meta-learning based few-shot classification framework for DAS data.
To investigate the impact of different pre-processing techniques on classification performance.

Main Methods:

Implemented a neural network model utilizing meta-learning for feature extraction and classification.
Explored three pre-processing methods: decomposed phase, power spectral density, and frequency energy band.
Developed a few-shot classification framework for classifying query samples using limited support samples.

Main Results:

The embedding model demonstrated efficient learning capabilities across various pre-processed DAS data.
Achieved outstanding few-shot classification performance for a large number of event classes.
The framework showed adaptability with different pre-processing techniques.

Conclusions:

The proposed meta-learning framework is effective for few-shot classification of DAS data.
The study provides valuable insights into pre-processing strategies for DAS.
The framework holds significant potential for real-world urban infrastructure monitoring applications.