首个无人机多光谱排雷数据集，支持高效安全的智能探测研究
AMLID: An Adaptive Multispectral Landmine Identification Dataset for Drone-Based Detection
- 融合RGB与红外影像，构建多维度排雷数据集
- 覆盖21类地雷、11种光谱融合等级，含4种飞行高度和3种光照条件
- 适合研究者训练自适应算法，推动人道排雷技术普惠
地雷仍是全球60个国家中持续的人道威胁，据估计有1.1亿枚地雷分布，每年造成约2.6万伤亡。现有探测方法危险、低效且成本高昂。本文提出首个开源的无人机多光谱排雷数据集AMLID，整合了红绿蓝（RGB）与长波红外（LWIR）影像，用于无人航空系统（UAS）地雷检测。AMLID包含12,078张标注图像，涵盖21种全球部署的地雷类型，包括反人员与反坦克类别，且分别具有金属与塑料材质。数据集覆盖11种RGB-LWIR融合层级、4种传感器飞行高度、2个季节周期及3种日间光照条件。通过提供跨多种环境变量的全面多光谱覆盖，AMLID使研究者可在无需接触实弹或昂贵数据采集设备的情况下开发和评估自适应探测算法，从而推动人道排雷研究的普及化。
原文摘要 · Abstract (English)
Landmines remain a persistent humanitarian threat, with an estimated 110 million mines deployed across 60 countries, claiming approximately 26,000 casualties annually. Current detection methods are hazardous, inefficient, and prohibitively expensive. We present the Adaptive Multispectral Landmine Identification Dataset (AMLID), the first open-source dataset combining Red-Green-Blue (RGB) and Long-Wave Infrared (LWIR) imagery for Unmanned Aerial Systems (UAS)-based landmine detection. AMLID comprises of 12,078 labeled images featuring 21 globally deployed landmine types across anti-personnel and anti-tank categories in both metal and plastic compositions. The dataset spans 11 RGB-LWIR fusion levels, four sensor altitudes, two seasonal periods, and three daily illumination conditions. By providing comprehensive multispectral coverage across diverse environmental variables, AMLID enables researchers to develop and benchmark adaptive detection algorithms without requiring access to live ordnance or expensive data collection infrastructure, thereby democratizing humanitarian demining research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。