Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm

2021 ◽  
Vol 273 ◽  
pp. 121949
Author(s):  
Shuwei Li ◽  
Xingyu Gu ◽  
Xiangrong Xu ◽  
Dawei Xu ◽  
Tianjie Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ground Penetrating Radar ◽  
Learning Algorithm ◽  
Radar Images ◽  
Deep Learning Algorithm ◽  
Ground Penetrating

scholarly journals Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

2021 ◽  
Vol 13 (22) ◽  
pp. 4590
Author(s):  
Yunpeng Yue ◽  
Hai Liu ◽  
Xu Meng ◽  
Yinguang Li ◽  
Yanliang Du
Keyword(s):  
Deep Learning ◽  
High Precision ◽  
Ground Penetrating Radar ◽  
Data Augmentation ◽  
Least Square ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Radar Images ◽  
Adversarial Networks ◽  
Ground Penetrating

Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

Sign in / Sign up

Export Citation Format

Share Document