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

ABSTRACTDetection 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 mAP@0.5 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.

• Record URL:
  • https://doi.org/10.1080/10298436.2022.2155648
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/44544515
• Supplemental Notes:
  • © 2022 Informa UK Limited, trading as Taylor & Francis Group 2022. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Xiong, Xuetang
  • Tan, Yiqiu
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 2155648
• Serial:
  • International Journal of Pavement Engineering
  • Volume: 24
  • Issue Number: 2
  • Publisher: Taylor & Francis
  • ISSN: 1029-8436
  • Serial URL: http://www.tandf.co.uk/journals/titles/10298436.html

Subject/Index Terms

• TRT Terms: Concrete pavements; Ground penetrating radar; Machine learning; Tie bars
• Identifier Terms: YOLO
• Subject Areas: Highways; Pavements;

Filing Info

• Accession Number: 01911242
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 10 2024 4:16PM