融合可见光与红外图像，用YOLO模型提升无人机探测地雷的精度与效率。
Multi-temporal Adaptive Red-Green-Blue and Long-Wave Infrared Fusion for You Only Look Once-Based Landmine Detection from Unmanned Aerial Systems
- 基于YOLO架构，自适应融合可见光与红外图像特征。
- YOLOv11在86.8% mAP下表现最优，5-10米高度、10%-30%热成像融合为最佳参数。
- 多时相数据训练效果更优，适合实际部署中需兼顾速度与准确性的场景。
地雷仍是全球60个国家中1.1亿枚活跃地雷带来的持续人道威胁，每年造成2.6万伤亡。本研究评估了无人机平台基于自适应红绿蓝（RGB）与长波红外（LWIR）融合的表面布设地雷检测方法，利用弹药与土壤间的热对比增强特征提取。采用YOLOv8、v10、v11在114张测试图像上生成35,640次模型评估，结果表明YOLOv11性能最优（mAP 86.8%），5–10米高度下10%–30%热融合为最佳检测参数。对比显示，尽管RF-DETR准确率最高（69.2% mAP），但其训练耗时12小时，远高于YOLOv11的41分钟（快17.7倍），形成关键精度-效率权衡。多时相联合训练集比季节特异性方法提升1.8%至9.6%，表明模型受益于多样热环境。反坦克（AT）地雷检测率达61.9%，远超反人员（AP）地雷的19.2%，反映尺寸与热容差异的影响。由于仅针对表面布设地雷，未来应量化不同埋深及土壤类型下的热对比效应。
原文摘要 · Abstract (English)
Landmines remain a persistent humanitarian threat, with 110 million actively deployed mines across 60 countries, claiming 26,000 casualties annually. This research evaluates adaptive Red-Green-Blue (RGB) and Long-Wave Infrared (LWIR) fusion for Unmanned Aerial Systems (UAS)-based detection of surface-laid landmines, leveraging the thermal contrast between the ordnance and the surrounding soil to enhance feature extraction. Using You Only Look Once (YOLO) architectures (v8, v10, v11) across 114 test images, generating 35,640 model-condition evaluations, YOLOv11 achieved optimal performance (86.8% mAP), with 10 to 30% thermal fusion at 5 to 10m altitude identified as the optimal detection parameters. A complementary architectural comparison revealed that while RF-DETR achieved the highest accuracy (69.2% mAP), followed by Faster R-CNN (67.6%), YOLOv11 (64.2%), and RetinaNet (50.2%), YOLOv11 trained 17.7 times faster than the transformer-based RF-DETR (41 minutes versus 12 hours), presenting a critical accuracy-efficiency tradeoff for operational deployment. Aggregated multi-temporal training datasets outperformed season-specific approaches by 1.8 to 9.6%, suggesting that models benefit from exposure to diverse thermal conditions. Anti-Tank (AT) mines achieved 61.9% detection accuracy, compared with 19.2% for Anti-Personnel (AP) mines, reflecting both the size differential and thermal-mass differences between these ordnance classes. As this research examined surface-laid mines where thermal contrast is maximized, future research should quantify thermal contrast effects for mines buried at varying depths across heterogeneous soil types.
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。