A Novel Method of Hyperbola Recognition in Ground Penetrating Radar (GPR) B-Scan Image for Tree Roots Detection

Abstract

Ground penetrating radar (GPR), as a newly nondestructive testing technology (NDT), has been adopted to explore the spatial position and the structure of the tree roots. Due to the complexity of soil distribution and the randomness of the root position in the natural environment, it is difficult to locate the root in the GPR B-Scan image. In this study, a novel method for root detection in the B-scan image by considering both multidirectional features and symmetry of hyperbola was proposed. Firstly, a mixed dataset B-Scan images were employed to train Faster RCNN (Regions with CNN features) to obtain the potential hyperbola region. Then, the peak area and its connected region were filtered from the four directional gradient graphs in the proposed region. The symmetry test was applied to segment the intersecting hyperbolas. Finally, two rounds of coordinate transformation and line detection based on Hough transform were employed for the hyperbola recognition and root radius and position estimation. To validate the effectiveness of this approach for tree root detection, a mixed dataset was made, including synthetic data from gprMax as well as field data collected from 35 ancient tree roots and fresh grapevine controlled experiments. From the results of hyperbola recognition as well as the estimation for the radius and position of the root, our method shows a significant effect in root detection.

Training data augmentations for improving hyperbola recognition in ground penetrating radar B-scan image for tree roots detection

“…Wen et al (2020) proposed a shearlet transform to perform noise removal from B-Scan images and achieved better denoising results in some image evaluation metrics. Deep learning models are being widely used to detect the internal structure of tree root systems (Xiang et al 2019;Hou et al 2021;Zhang et al 2021). Hou et al (2021) proposed using MS R-CNN architecture for the detection of GPR subsurface scanned objects, while using the transfer learning technique to obtain pre-trained models to solve the problem of the insufficient model training set (93 GPR root scans).…”

“…Hou et al (2021) proposed using MS R-CNN architecture for the detection of GPR subsurface scanned objects, while using the transfer learning technique to obtain pre-trained models to solve the problem of the insufficient model training set (93 GPR root scans). Zhang et al (2021) used the Faster R-CNN to train 1442 GPR B-scan images (282 for real images and 1160 for simulation images) to achieve automatic recognition and localization of hyperbolas in GPR images. It is not easy to perform effective automatic detection of tree root systems with these methods.…”