Deep Learning-Based Subsurface Target Detection From GPR Scans

Ground penetrating radar (GPR) has been widely used as a non-destructive technique to detect subsurface objects. Manual interpretation of GPR data is tedious and time-consuming. To address this challenge, an automatic method based on a deep instance segmentation framework is developed to detect and segment object signatures from GPR scans. The proposed method develops the Mask Scoring R-CNN (MS R-CNN) architecture by introducing a novel anchoring scheme. By analyzing the characteristics of the hyperbolic signatures of subsurface objects in GPR scans, a set of anchor shape ratios are optimized and selected to substitute the predefined and fixed aspect ratios in the MS R-CNN framework to improve the signature detection performance. In addition, transfer learning technique is adopted to obtain a pre-trained model to address the challenge of insufficient GPR dataset for model training. The detected and segmented signatures can then be further processed for target localization and characterization. GPR data of tree roots were collected in the field to validate the proposed methods. Despite the noisy background and varying signatures in the GPR scans, the proposed method demonstrated promising results in object detection and segmentation. Computational results show that the improved MS R-CNN outperforms the other state-of-the-art methods.

Automated summary

The automatic method based on deep instance segmentation framework improves the efficiency and accuracy of GPR data interpretation by optimizing anchor shape ratios and adopting transfer learning technique. The proposed method outperforms other state-of-the-art methods in object detection and segmentation.