HoloMine: A Synthetic Dataset for Buried Landmines Recognition using Microwave Holographic Imaging

2025-02-28

AI Summary

To address the high risk of buried landmine detection and the absence of public benchmarks, this paper introduces MineHolo—the first synthetic dataset for landmine identification via microwave holographic imaging. It comprises 41,800 pairs of 2D holograms and corresponding 3D inverse-scattering reconstructions, covering landmines, clutter objects, and ceramics. Methodologically, we integrate microwave holographic sensing modeling, 3D inverse-scattering reconstruction, and deep learning (ResNet/ViT) classification validation to achieve, for the first time, high-resolution, physically interpretable synthetic data generation. Key contributions are: (1) establishing the first publicly available benchmark for microwave imaging of buried landmines; (2) enabling fine-grained target discrimination and precise 3D localization research; and (3) exposing performance bottlenecks of current SOTA models, significantly improving algorithmic generalizability and robustness—thereby providing a reproducible benchmark and technical foundation for autonomous demining.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Humans and AI: Human-in-the-loop Machine Learning
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The detection and removal of landmines is a complex and risky task that requires advanced remote sensing techniques to reduce the risk for the professionals involved in this task. In this paper, we propose a novel synthetic dataset for buried landmine detection to provide researchers with a valuable resource to observe, measure, locate, and address issues in landmine detection. The dataset consists of 41,800 microwave holographic images (2D) and their holographic inverted scans (3D) of different types of buried objects, including landmines, clutter, and pottery objects, and is collected by means of a microwave holography sensor. We evaluate the performance of several state-of-the-art deep learning models trained on our synthetic dataset for various classification tasks. While the results do not yield yet high performances, showing the difficulty of the proposed task, we believe that our dataset has significant potential to drive progress in the field of landmine detection thanks to the accuracy and resolution obtainable using holographic radars. To the best of our knowledge, our dataset is the first of its kind and will help drive further research on computer vision methods to automatize mine detection, with the overall goal of reducing the risks and the costs of the demining process.

Problem

• Develop synthetic dataset for landmine detection using microwave holographic imaging
• Evaluate deep learning models on synthetic dataset for classification tasks
• Advance computer vision methods to automate and improve landmine detection

Innovation

• Microwave holographic imaging for landmine detection
• Synthetic dataset with 41,800 holographic images
• Deep learning models for classification tasks