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

Detection and clearance of landmines is a complex and risky activity that requires advanced remote sensing techniques to reduce the risk to operators in the field. In this article, 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 (2-D) and their holographic inverted scans (3-D) of different types of buried objects, including landmines, clutter, and pottery objects, and is collected by means of a microwave holography radar. 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 the holographic radars. To the best of the authors' knowledge, the 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.