北京林业大学学报2024，Vol.46Issue(4)：103-114,12.DOI:10.12171/j.1000-1522.20230259
基于深度学习的树木根系探地雷达多目标参数反演识别
Deep learning-based inverse identification of multi-target parameters for tree rooting ground-penetrating radar
摘要
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.关键词
探地雷达/多参数估计/无损检测/多任务学习Key words
ground penetrating radar/multi-parameter estimation/nondestructive testing/multi-task learning分类
林学引用本文复制引用
李爽,张潇巍,谭旭,徐凌飞,吕生华,文剑..基于深度学习的树木根系探地雷达多目标参数反演识别[J].北京林业大学学报,2024,46(4):103-114,12.基金项目
国家自然科学基金项目(32071679),北京市自然科学基金项目(6202023). （32071679）
