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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Predictive Maintenance of Norwegian Road Network Using Deep Learning Models.

Muhammad Umair Hassan¹, Ole-Martin Hagen Steinnes¹, Eirik Gribbestad Gustafsson¹

¹Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

This study introduces a deep learning approach for predictive maintenance (PdM) of roads. It effectively detects and classifies road damage, enabling prioritized maintenance decisions for improved infrastructure management.

Keywords:

anomaly detection deep learning highway predictive maintenance predictive maintenance

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Area of Science:

Civil Engineering
Computer Science
Artificial Intelligence

Background:

Industry 4.0 drives digitalization in asset management.
Predictive maintenance (PdM) is crucial for road network upkeep.
Current methods require efficient road damage detection.

Purpose of the Study:

To develop a deep learning-based PdM framework for road maintenance.
To effectively recognize and classify various road crack types and damages.
To enable data-driven prioritization of road maintenance decisions.

Main Methods:

Utilized pre-trained deep learning models for road damage detection.
Trained neural networks to classify road deterioration, including cracks, corrugation, and potholes.
Developed a framework to determine degradation percentage and damage intensity.

Main Results:

Achieved significant performance in recognizing and detecting road crack types.
Effectively classified roads based on the amount and severity of damage.
Demonstrated the framework's capability to prioritize maintenance actions.

Conclusions:

The proposed deep learning framework offers an efficient PdM solution for road infrastructure.
Enables inspection authorities and stakeholders to make informed maintenance decisions.
Contributes to optimized road network management through advanced damage assessment.