What question did this study set out to answer?

This research aims to enhance the detection accuracy of concealed distresses in asphalt pavements using a combination of 3D-GPR technology and deep learning.

August 2, 2026Nondestructive Testing And Evaluation

Electromagnetic wave echo characteristic atlas and automatic recognition of internal distresses in asphalt pavement combined with 3D-GPR and deep learning

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Authors

XXXuetang Xiong XCXuran Cai ZFZhihong Fan

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Overview

Randomized trial demonstrates improved accuracy in recognizing internal distresses in asphalt pavement, highlighting the utility of deep learning methods.

Key Points

This research aims to enhance the detection accuracy of concealed distresses in asphalt pavements using a combination of 3D-GPR technology and deep learning.
Constructed a reference atlas of 3D-GPR echo characteristics for typical internal distresses.
Developed a dataset with 2,983 internal crack instances and 1,274 debonding instances using real and simulated GPR images.
Trained and validated 20 YOLO-series models on the augmented dataset to improve recognition accuracy.
YOLOv8s achieved the best recognition accuracy of 98.7% mAP@0.5 and a processing speed of 111.1 frames per second.
The proposed methodology significantly enhances both accuracy and efficiency in identifying internal distresses compared to other YOLO variants.

Cite This Study

Xiong et al. (2026) studied this question.

synapsesocial.com/papers/6a6eea811b0468a7eeab2cd9 https://doi.org/10.1080/10589759.2026.2708139

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August 2, 2026Nondestructive Testing And Evaluation

Electromagnetic wave echo characteristic atlas and automatic recognition of internal distresses in asphalt pavement combined with 3D-GPR and deep learning

View Full Paper

Ask AI

Bookmark

Authors

XXXuetang Xiong XCXuran Cai ZFZhihong Fan

Discussion

Member takes

Overview

Randomized trial demonstrates improved accuracy in recognizing internal distresses in asphalt pavement, highlighting the utility of deep learning methods.

Key Points

This research aims to enhance the detection accuracy of concealed distresses in asphalt pavements using a combination of 3D-GPR technology and deep learning.
Constructed a reference atlas of 3D-GPR echo characteristics for typical internal distresses.
Developed a dataset with 2,983 internal crack instances and 1,274 debonding instances using real and simulated GPR images.
Trained and validated 20 YOLO-series models on the augmented dataset to improve recognition accuracy.
YOLOv8s achieved the best recognition accuracy of 98.7% mAP@0.5 and a processing speed of 111.1 frames per second.
The proposed methodology significantly enhances both accuracy and efficiency in identifying internal distresses compared to other YOLO variants.

Cite This Study

Xiong et al. (2026) studied this question.

synapsesocial.com/papers/6a6eea811b0468a7eeab2cd9 https://doi.org/10.1080/10589759.2026.2708139

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