Deep learning-based detection of tie bars in concrete pavement using ground penetrating radar

ABSTRACT

Detection of the tie bars in concrete pavement has been a challenging task. To address the purpose, ground penetrating radar (GPR) was used to acquire a large amount of image data along the longitudinal construction joints of plain concrete pavement in the field. The GPR image data was filtered to construct the dataset, containing 2185 tie bar reflected waves in 670 GPR images. Then, the YOLO series models, as the deep learning algorithms applied in inspecting the tie bars from GPR images, were well trained with the GPR training and validation sets. The comprehensive detection accuracy of the YOLOv4 model outperforms the YOLOv3, YOLOv3-tiny, and YOLOv4-tiny models in the test set. The [email protected] value of the YOLOv4 model can reach 99.74%. All the signatures of tie bars in the testing GPR images, no matter whether they are incomplete, compressed, blurry with missing signal, or strong background noise, can be correctly and completely anchored using the bounding box based on the YOLOv4 model. Meanwhile, the detection speed of the YOLOv4 model for GPR data video is 50.8 frames per second. Therefore, the YOLOv4 model is reliable for automatically detecting the tie bars from GPR data in real-time.

KEYWORDS:

• Concrete pavement
• tie bars
• GPR
• YOLO series models
• deep learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China joint fund for regional innovation and development: [Grant Number U20A20315]; Open Fund of Key Laboratory of Road and Bridge Detection and Maintenance Technology of Zhejiang Province: [Grant Number 202203Z]; Open Research Fund Program of Guangdong Key Laboratory of Urban Informatics: [Grant Number SZU51029202005].