构建首个微波全息成像地雷数据集，助力智能探测研究
HoloMine: A Synthetic Dataset for Buried Landmines Recognition using Microwave Holographic Imaging
- 用微波全息传感器生成41800张2D/3D合成图像
- 涵盖地雷、杂物等物体，支持多类型分类任务
- 为地雷探测提供高精度数据资源，适合计算机视觉研究
地雷探测与清除是一项复杂且高风险的任务，需依赖先进的遥感技术以降低专业人员的风险。本文提出一个全新的合成数据集，用于埋藏地雷检测，为研究人员提供观察、测量和定位地雷的宝贵资源。该数据集包含41,800张不同类型的埋藏物体（包括地雷、杂乱物和陶器）的二维微波全息图像及其三维全息反演扫描图，由微波全息传感器采集。我们评估了多个前沿深度学习模型在该合成数据集上进行各类分类任务的性能。尽管当前结果尚未达到高精度，反映出任务的挑战性，但我们认为该数据集凭借全息雷达可实现的高精度与分辨率，具有显著潜力推动地雷探测领域的发展。据我们所知，这是首个此类数据集，将促进计算机视觉方法在自动化地雷探测中的研究，最终目标是降低排雷风险与成本。
原文摘要 · Abstract (English)
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.
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