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 at diverse spectral ranges, including visible (RGB, monochrome), visible short-wave infrared (VIS-SWIR), and long-wave infrared (LWIR). Additionally, the dataset provides bounding boxes generated by an automated pipeline and refined with human supervision. We recorded approximately one hour of data in both daylight and nighttime conditions, resulting in around 38,000 RGB frames, 53,000 VIS-SWIR frames, and 108,000 LWIR frames. MineInsight serves as a benchmark for developing and evaluating landmine detection algorithms. Our dataset is available at https://github.com/mariomlz99/MineInsight.

翻译：在人道主义扫雷中，机器人技术的应用日益依赖计算机视觉技术以提升地雷探测能力。然而，由于缺乏多样且真实的数据集，算法的可靠验证仍是研究界面临的挑战。本文介绍了MineInsight——一个公开可用的多传感器、多光谱数据集，专为野外环境地雷探测设计。该数据集包含35个不同目标（15枚地雷和20个常见物体），分布在三条独立路径上，提供了多样且真实的测试环境。据我们所知，MineInsight是首个集成无人地面车辆及其机械臂双视角传感器扫描的数据集，通过多视角数据缓解遮挡问题并提升空间感知能力。数据集配备两台激光雷达，以及覆盖多个光谱范围的图像，包括可见光（RGB、单色）、可见光-短波红外（VIS-SWIR）和长波红外（LWIR）。此外，数据集提供通过自动化流程生成并经人工校核优化的边界框标注。我们在日间和夜间条件下记录了约一小时数据，共获得约38,000帧RGB图像、53,000帧VIS-SWIR图像及108,000帧LWIR图像。MineInsight可作为地雷探测算法开发与评估的基准。本数据集发布于https://github.com/mariomlz99/MineInsight。