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An Architectural Multi-Agent System for a Pavement Monitoring System with Pothole Recognition in UAV Images.

Luís Augusto Silva^1,2, Héctor Sanchez San Blas¹, David Peral García¹

¹Expert Systems and Applications Lab-ESALAB, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain.

Sensors (Basel, Switzerland)

|November 4, 2020

Summary

This study introduces a drone-based system for detecting road damage, improving safety and reducing maintenance costs. Utilizing deep learning, the platform achieves over 95% accuracy in identifying road surface defects.

Keywords:

YOLOv4 crack detection drones smart applications virtual organizations of agents

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Area of Science:

Civil Engineering
Computer Science
Artificial Intelligence

Background:

Decreased maintenance on public transport routes correlates with increased accidents due to poor road conditions.
Current manual road damage detection methods are labor-intensive and costly.
Image processing and deep learning offer potential solutions for automated road defect identification.

Purpose of the Study:

To design a distributed platform for detecting transport route damage using drones.
To evaluate the performance of various deep learning classifiers for road damage detection.
To provide a reliable and cost-effective automated solution for road infrastructure assessment.

Main Methods:

Development of a distributed platform integrating drones and ubiquitous computing.
Implementation of a multi-agent system (PANGEA) for architectural coordination.
Customization and application of the You Only Look Once (YOLO) v4 classifier for image analysis.

Main Results:

The YOLO v4 classifier achieved an accuracy exceeding 95% in detecting road damage.
The developed platform effectively coordinates drone-based data acquisition and analysis.
A dataset of road images was created and published for community use.

Conclusions:

The drone-based, deep learning approach offers a highly accurate and efficient method for road damage detection.
This technology can significantly improve road safety and optimize maintenance strategies.
The distributed platform and open dataset contribute to advancing research in intelligent transportation systems.