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Updated: Jul 9, 2025

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

UAV-Based Image and LiDAR Fusion for Pavement Crack Segmentation.

Ahmed Elamin^1,2, Ahmed El-Rabbany¹

¹Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This study fuses drone imaging and LiDAR data to improve pavement crack detection. Combining elevation data with images enhanced crack segmentation, showing promise for road safety maintenance.

Keywords:

LiDAR UAV crack detection deep convolutional neural network pixel-level

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Last Updated: Jul 9, 2025

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Area of Science:

Civil Engineering
Computer Vision
Remote Sensing

Background:

Manual pavement inspection is time-consuming and labor-intensive.
Existing unmanned aerial system (UAS) methods use only image or LiDAR data, missing complementary information.
Developing automated, accurate pavement distress detection is crucial for road safety and maintenance.

Purpose of the Study:

To explore the feasibility of fusing UAS-based imaging and low-cost LiDAR data for enhanced pavement crack segmentation.
To evaluate the performance of a deep convolutional neural network (DCNN) model using fused data.
To investigate the impact of different data fusion combinations (RGB, intensity, elevation) on crack and sealed crack detection.

Main Methods:

Collected three datasets using two UASs at varying altitudes.
Investigated two pavement distress types: cracks and sealed cracks.
Employed a modified U-net DCNN with residual blocks for segmentation.
Compared four fusion combinations: RGB, RGB + intensity, RGB + elevation, RGB + intensity + elevation.

Main Results:

The DCNN model demonstrated superior accuracy and generalizability compared to state-of-the-art networks.
Fusing elevation data with RGB images increased recall by 2% for crack detection.
LiDAR intensity data fusion reduced precision, recall, and F-measure due to sensor limitations.
LiDAR data improved sealed crack segmentation by 4-7% across datasets.
Higher resolution LiDAR data at lower altitudes improved crack detail detection but decreased precision.

Conclusions:

Fusing UAS-based imaging and LiDAR data, particularly elevation, can enhance pavement crack segmentation.
The effectiveness of LiDAR data fusion depends on data quality and the type of pavement distress.
The developed DCNN model shows significant potential for automated pavement condition assessment.
Further research with higher-quality LiDAR data could overcome current limitations and improve fusion benefits.