Deep learning-based inverse identification of multi-target parameters for tree rooting ground-penetrating radar

Li Shuang; Zhang Xiaowei; Tan Xu; Xu Lingfei; Lü Shenghua; Wen Jian

doi:10.12171/j.1000-1522.20230259

Journal of Beijing Forestry University > 2024 > 46(4): 103-114. > DOI: 10.12171/j.1000-1522.20230259

Li Shuang, Zhang Xiaowei, Tan Xu, Xu Lingfei, Lü Shenghua, Wen Jian. Deep learning-based inverse identification of multi-target parameters for tree rooting ground-penetrating radar[J]. Journal of Beijing Forestry University, 2024, 46(4): 103-114. DOI: 10.12171/j.1000-1522.20230259

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Deep learning-based inverse identification of multi-target parameters for tree rooting ground-penetrating radar

School of Technology, Beijing Forestry University, Beijing 100083, China

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Received Date: September 26, 2023
Revised Date: December 30, 2023
Available Online: April 14, 2024

Graphical Abstract

Abstract

Abstract

Objective
This paper takes multi-target detection and multi-parameter estimation of root radar images using deep learning methods.
Method
In this study, a network model with YOLOv5s and CNN-LSTM as the main framework was constructed to achieve multi-target detection and multi-parameter estimation of root radar scanning images. Firstly, the root system radar profile data required for the experiments were obtained through simulation and pre-embedding experiments, and at the same time, in order to increase the diversity of the data, a batch of simulation data with the characteristics of real radar images were obtained using CycleGAN style migration network; then, the root system response region was identified and extracted using the YOLOv5s target detection network; then, the frequency domain transform was introduced to obtain the frequency domain features and fuse the time domain features and frequency domain features of the root system radar image; finally, a convolutional neural network (CNN), a convolutional attention mechanism, and a long short-term memory network (LSTM) were used to emphasize and extract the information features related to the root system parameters, and a multi-task learning approach was used to achieve the prediction of the root system radius, depth, relative permittivity, and horizontal angle.
Result
(1) In the simulation experiments, the maximum error in the estimation of root radius was 4.3 mm with R² of 0.980 and a root mean square error of 1.32. The maximum error in the estimation of depth was 35.1 mm with R² of 0.962 and a root mean square error of 17.68, and the maximum error in the estimation of relative permittivity was 3.1 with R² of 0.960 and a root mean square error of 1.10, and the maximum error in the estimation of the horizontal angle was 10.2°, with R² of 0.821 and the root mean square error was 4.96. (2) The average relative error of root radius estimation on measured data was 9.112%, the average relative error of depth estimation was 5.772%, and the average relative error of horizontal angle estimation was 11.25%.
Conclusion
The experimental data show that the method proposed in this paper can facilitate root detection and root parameter estimation.
- ground penetrating radar,
- multi-parameter estimation,
- nondestructive testing,
- multi-task learning

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References (33)

References

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Deep learning-based inverse identification of multi-target parameters for tree rooting ground-penetrating radar

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