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Design Example: Strain Gauge Bridge or Wheatstone Bridge

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Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck.

Rujin Ma¹, Zhen Zhang¹, Yiqing Dong¹

¹College of Civil Engineering, Tongji University, Siping Road 1239, Shanghai 200092, China.

Sensors (Basel, Switzerland)

|September 9, 2020

Summary

This summary is machine-generated.

This study introduces a novel method for vehicle detection and classification using bridge strain data, offering non-disruptive traffic monitoring. The approach accurately identifies and categorizes vehicles even in noisy environments.

Keywords:

Cascade filtering artificial neural network deep learning strain data vehicle classification vehicle detection

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Area of Science:

Engineering
Data Science
Transportation Science

Background:

Vehicle detection and classification are crucial for traffic management and infrastructure assessment.
Existing sensors often require traffic interruption or are susceptible to noise in complex road scenes.

Purpose of the Study:

To develop a data-driven methodology for non-intrusive vehicle detection and classification using strain data.
To address challenges posed by traffic interruption and signal noise in current methods.

Main Methods:

Utilizing strain sensors placed under a bridge deck to avoid traffic disruption.
Implementing a cascade pre-processing technique for noise elimination in vehicle detection.
Employing a neural network with Non-Maximum Suppression for identifying and separating close-following vehicles.
Designing a deep convolutional neural network for vehicle type classification based on axle groups.

Main Results:

The methodology demonstrated high robustness and accuracy in vehicle detection and classification on a long-span bridge.
Strain data collected over a week from three sensors yielded reliable results.
The system effectively handled complex noise conditions inherent in real-world traffic scenarios.

Conclusions:

The proposed data-driven method offers an adaptive and promising solution for vehicle detection and classification.
This approach serves as a valuable supplement to existing transportation systems.
Provides reliable data for enhanced traffic management and decision-making.