MineInsight: A Multi-sensor Dataset for Humanitarian Demining Robotics in Off-Road Environments
Mario Malizia, Charles Hamesse, Ken Hasselmann, Geert De Cubber, Nikolaos Tsiogkas, Eric Demeester, Rob Haelterman

TL;DR
MineInsight is a comprehensive multi-sensor dataset for off-road landmine detection, featuring diverse targets, multiple viewpoints, and spectral data to advance humanitarian demining robotics research.
Contribution
This paper introduces MineInsight, the first dataset to combine dual-view sensor scans from ground robots and robotic arms for landmine detection in off-road environments.
Findings
Provides a diverse set of 35 targets including landmines and objects
Includes multi-spectral data from RGB, VIS-SWIR, and LWIR sensors
Offers automated and human-refined bounding boxes for detection tasks
Abstract
The use of robotics in humanitarian demining increasingly involves computer vision techniques to improve landmine detection capabilities. However, in the absence of diverse and realistic datasets, the reliable validation of algorithms remains a challenge for the research community. In this paper, we introduce MineInsight, a publicly available multi-sensor, multi-spectral dataset designed for off-road landmine detection. The dataset features 35 different targets (15 landmines and 20 commonly found objects) distributed along three distinct tracks, providing a diverse and realistic testing environment. MineInsight is, to the best of our knowledge, the first dataset to integrate dual-view sensor scans from both an Unmanned Ground Vehicle and its robotic arm, offering multiple viewpoints to mitigate occlusions and improve spatial awareness. It features two LiDARs, as well as images captured…
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Taxonomy
TopicsGeophysical Methods and Applications · Fire Detection and Safety Systems · Microwave Imaging and Scattering Analysis
