Open AccessIntelligent Detection of Urban Road Underground Targets by Using Ground Penetrating Radar based on Deep Learning
Author(s) -
Yao Gao,
Lili Pei,
Sa Wang,
Wei Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012081
Subject(s) - ground penetrating radar , convolutional neural network , radar , computer science , artificial intelligence , pipeline transport , radar imaging , remote sensing , pipeline (software) , computer vision , artificial neural network , geology , engineering , telecommunications , environmental engineering , programming language
Ground penetrating radar (GPR) is widely used in the field of intelligent road detection because of its non-destructive detection method, which is based on an electromagnetic wave reflection mechanism. However, this method requires large-scale data processing and also relies on manual judgment, which is time-consuming and laborious. To solve this problem, this paper analyzes and studies the GPR images of underground pipelines and uneven settlement of urban roads by means of road surface measurement and laboratory tests. The radar image data set is constructed by collecting radar images and denoising and marking them. Then, Deep Feature Selection Net is adopted to improve the fast region-based convolutional neural network (Faster R-CNN) to enhance the network’s ability to extract features from radar images. Finally, by comparing with the improved faster R-CNN model, it is found that the automatic identification rate of underground pipelines and uneven settlement in urban roads increases, reaching more than 80%.