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A Road Defect Detection System Using Smartphones.

Gyulim Kim¹, Seungku Kim¹

¹Electronics Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This study introduces a smartphone-based system for automatic road defect detection. It uses deep learning to accurately identify potholes and other road issues, making infrastructure maintenance more efficient.

Keywords:

CNN automatic data collection road defect detection smartphone

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Area of Science:

Computer Science
Civil Engineering
Transportation Engineering

Background:

Road defect detection is crucial for infrastructure maintenance and public safety.
Existing methods for data collection and analysis are often manual, time-consuming, and costly.
Smartphones offer a ubiquitous and cost-effective platform for data acquisition.

Purpose of the Study:

To develop an automated system for road defect detection using smartphones.
To create a practical and reliable data collection mechanism for road defect research.
To design and evaluate a deep learning model for classifying common road defects.

Main Methods:

An automatic data collection mechanism was developed to gather road imagery and associated data.
A Convolutional Neural Network (CNN)-based deep learning model was designed for defect classification.
The model was trained and validated using the automatically collected dataset.
Performance was compared against conventional road defect detection models.

Main Results:

The proposed system demonstrated effective automatic data collection and labeling.
The CNN-based model accurately classified speed bumps, manholes, and potholes.
The developed model outperformed conventional methods in both detection accuracy and processing speed.
The system proved to be practical and scalable for implementation on commercial smartphones.

Conclusions:

The novel smartphone-based approach offers a practical and efficient solution for road defect detection.
The automated data collection and deep learning model significantly advance road defect research.
This technology holds substantial promise for real-world road infrastructure monitoring and maintenance applications.