Open AccessRoadbed defect detection from ground penetrating radar B-scan data using Faster RCNN
Author(s) -
Zheng Fang,
Zhibin Shi,
Xiaokai Wang,
Wenchao Chen
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/660/1/012020
Subject(s) - ground penetrating radar , computer science , subgrade , radar , artificial intelligence , pattern recognition (psychology) , engineering , geotechnical engineering , telecommunications
Ground penetrating radar (GPR) is the main technical method to detect roadbed subgrade defect. The recognition of subgrade defect is still mainly based on manual interpretation, which requires high professional knowledge of interpreters, resulting in the demand for automatic detection technology. In this paper, a solution for automatic detection of roadbed defect by implementing Faster RCNN with GPR system is presented. We simulated 30000 roadbed defect GPR B-scan data by simulation software gprMax, labeled them appropriately and automatically. Specifically, Faster RCNN was chosen, as a compromise between accuracy and ease of comparison. Preliminary detection results show that the AP (Average Precision) is 0.8067, proving that our simulation for defect is reasonable and reliable. And the Faster RCNN trained on the simulation dataset without any actual data also has excellent performance on the actual GPR data. Our method of detecting defects automatically with CNN can be easily generalized to other GPR tasks, e.g., detecting pipe, horizon extraction. It’s the biggest open-source GPR B-scan dataset as far as we know. Our simulation dataset and trained model will be made available.