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UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks.

Daegyun Choi¹, William Bell², Donghoon Kim¹

¹Department of Aerospace Engineering & Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221, USA.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This summary is machine-generated.

This study introduces a framework using unmanned aerial vehicles (UAVs) and deep learning to detect structural cracks and their locations. This method enhances public safety by enabling early detection of critical defects in aging infrastructure.

Keywords:

convolutional neural network crack detection deep learning image processing unmanned aerial vehicle

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Area of Science:

Civil Engineering
Computer Science
Structural Health Monitoring

Background:

Structural cracks are critical indicators of aging infrastructure health.
Early detection of cracks is essential for public safety, especially in highly trafficked areas.
Current visual inspection methods have limitations in resolution and scope.

Purpose of the Study:

To propose a framework for detecting structural cracks and determining their precise locations.
To leverage unmanned aerial vehicle (UAV) technology for infrastructure inspection.
To improve the accuracy and efficiency of crack detection in aging structures.

Main Methods:

Image stitching techniques were used to overcome camera resolution limitations.
A deep learning model was employed to identify cracks in the processed images.
Unmanned aerial vehicle (UAV) sensor data was utilized to determine crack locations.

Main Results:

The framework successfully detected cracks on an actual building using UAV-captured imagery.
The system accurately identified the presence and location of structural cracks.
The proposed method demonstrated effectiveness in crack detection and localization.

Conclusions:

The developed framework provides an effective solution for detecting and locating structural cracks.
This approach enhances the safety and maintenance of aging infrastructure.
UAVs combined with deep learning offer a promising method for structural health monitoring.