# Buried object detection from B-scan ground penetrating radar data using Faster-RCNN

arXiv: https://arxiv.org/abs/1803.08414 · HTML: https://mlsanity.com/paper/1803.08414/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [Scene-aware Learning Network for Radar Object Detection](https://mlsanity.com/paper/2107.01469/) (2021)
- [HoloMine: A Synthetic Dataset for Buried Landmines Recognition using Microwave Holographic Imaging](https://mlsanity.com/paper/2502.21054/) (2025)
- [Automated Object Behavioral Feature Extraction for Potential Risk Analysis based on Video Sensor](https://mlsanity.com/paper/2107.03554/) (2021)
- [Improving the Anomaly Detection in GPR Images by Fine-Tuning CNNs with Synthetic Data](https://mlsanity.com/paper/2210.11833/) (2022)
- [Lightweight framework for underground pipeline recognition and spatial localization based on multi-view 2D GPR images](https://mlsanity.com/paper/2512.20866/) (2025)
- [TriView-YOLO: Early Multi-View Fusion for Ground Penetrating Radar Cavity Detection in Soft, High-Water-Content Soils](https://mlsanity.com/paper/2608.09522/) (2026)

## Abstract (arXiv)

In this paper, we adapt the Faster-RCNN framework for the detection of underground buried objects (i.e. hyperbola reflections) in B-scan ground penetrating radar (GPR) images. Due to the lack of real data for training, we propose to incorporate more simulated radargrams generated from different configurations using the gprMax toolbox. Our designed CNN is first pre-trained on the grayscale Cifar-10 database. Then, the Faster-RCNN framework based on the pre-trained CNN is trained and fine-tuned on both real and simulated GPR data. Preliminary detection results show that the proposed technique can provide significant improvements compared to classical computer vision methods and hence becomes quite promising to deal with this kind of specific GPR data even with few training samples.