381 citations · 825 across the 6 of their papers we have counts for
6 papers
Improving Extrinsics between RADAR and LIDAR using Learning
Peng Jiang, Srikanth Saripalli
LIDAR and RADAR are two commonly used sensors in autonomous driving systems. The extrinsic calibration between the two is crucial for effective sensor fusion. The challenge arises…
Physics-driven Deep Learning Inversion for Direct Current Resistivity Survey Data
Bin Liu, Yonghao Pang, Peng Jiang +5
The direct-current (DC) resistivity method is a commonly used geophysical technique for surveying adverse geological conditions. Inversion can reconstruct the resistivity model fro…
Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network
Senlin Yang, Zhengfang Wang, Jing Wang +4
This research proposes a Ground Penetrating Radar (GPR) data processing method for non-destructive detection of tunnel lining internal defects, called defect segmentation. To perfo…
GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining
Bin Liu, Yuxiao Ren, Hanchi Liu +4
A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurfa…
Deep Learning Inversion of Electrical Resistivity Data
Bin Liu, Qian Guo, Shucai Li +6
The inverse problem of electrical resistivity surveys (ERSs) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still f…
Deep-Learning Inversion of Seismic Data
Shucai Li, Bin Liu, Yuxiao Ren +4
We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly…