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

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.

Error assessment of microwave holography inversion for shallow buried objects

Holographic imaging is a technique that uses microwave energy to create a three-dimensional image of an object or scene. This technology has potential applications in land mine detection, as the long-wavelength microwave energy can penetrate the ground and create an image of hidden objects without the need for direct physical contact. However, the inversion algorithms commonly used to digitally reconstruct 3D images from holographic images, such as Convolution, Angular Spectrum, and Fresnel, are known to have limitations and can introduce errors in the reconstructed image. Despite these challenges, the use of holographic radar at around 2 GHz in combination with holographic imaging techniques for land mine detection allows to recover size and shape of buried objects. In this paper, we estimate the reconstruction error for the convolution algorithm based on hologram imaging simulation and assess these errors recommending an increase in the scanner area, considering the limitations that the system has and the expected error reduction.