What question did this study set out to answer?

The research aims to compare the effectiveness of imagery, LiDAR, and accelerometer-based methods for detecting pavement anomalies.

July 24, 2026SensorsOpen Access

Road Surface Condition Evaluation Using Imaging, LiDAR, and Multi-Grade Navigation Systems

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Authors

AEAser M. EissaPurdue University West Lafayette MHMona HodaeiPurdue University West Lafayette RMRaja ManishPurdue University West Lafayette

Discussion

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Implication

Randomized trial evaluates the effectiveness of different technologies for detecting pavement anomalies, suggesting a scalable solution for road monitoring.

Key Points

The research aims to compare the effectiveness of imagery, LiDAR, and accelerometer-based methods for detecting pavement anomalies.
Evaluated a 5-mile urban roadway segment using all three sensing modalities under identical conditions.
Used manually interpreted reference anomalies for accuracy comparison between different detection methods.
Applied accelerometer-based methods across a 36-mile network for scalability assessment.
The accelerometer-based approach achieved an F1-score of 97.2%, indicating strong detection performance.
Spatial agreement among accelerometer systems exceeded 0.91 across the full route, with 962–996 surface defects detected.
LiDAR-based method achieved an F1-score of 93.0%, showing its viability as a complementary tool.

Cite This Study

Eissa et al. (2026) studied this question.

synapsesocial.com/papers/6a630179395161722cd16145 https://doi.org/10.3390/s26144645

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July 24, 2026SensorsOpen Access

Road Surface Condition Evaluation Using Imaging, LiDAR, and Multi-Grade Navigation Systems

View Full Paper

Ask AI

Bookmark

Authors

AEAser M. EissaPurdue University West Lafayette MHMona HodaeiPurdue University West Lafayette RMRaja ManishPurdue University West Lafayette

Discussion

Member takes

Implication

Randomized trial evaluates the effectiveness of different technologies for detecting pavement anomalies, suggesting a scalable solution for road monitoring.

Key Points

The research aims to compare the effectiveness of imagery, LiDAR, and accelerometer-based methods for detecting pavement anomalies.
Evaluated a 5-mile urban roadway segment using all three sensing modalities under identical conditions.
Used manually interpreted reference anomalies for accuracy comparison between different detection methods.
Applied accelerometer-based methods across a 36-mile network for scalability assessment.
The accelerometer-based approach achieved an F1-score of 97.2%, indicating strong detection performance.
Spatial agreement among accelerometer systems exceeded 0.91 across the full route, with 962–996 surface defects detected.
LiDAR-based method achieved an F1-score of 93.0%, showing its viability as a complementary tool.

Cite This Study

Eissa et al. (2026) studied this question.

synapsesocial.com/papers/6a630179395161722cd16145 https://doi.org/10.3390/s26144645

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