多时序自适应红绿蓝和长波红外融合用于无人机基于You Only Look Once的地雷检测
Multi-temporal Adaptive Red-Green-Blue and Long-Wave Infrared Fusion for You Only Look Once-Based Landmine Detection from Unmanned Aerial Systems
AI总结：
本研究通过多时序自适应RGB和LWIR融合技术，利用YOLOv11在无人机上实现高精度地雷检测，展示了在不同高度和热条件下的最佳参数，同时揭示了不同弹药类别的检测差异。
摘要：
地雷仍然是持续的人道主义威胁，全球有1.1亿枚正在部署的地雷分布在60个国家，每年造成26,000名遇难者。本研究评估了适用于无人机系统的自适应红绿蓝（RGB）和长波红外（LWIR）融合技术，用于表面地雷的检测，利用弹药与周围土壤之间的热对比来增强特征提取。使用You Only Look Once（YOLO）架构（v8、v10、v11）在114张测试图像上进行训练，生成35,640次模型条件评估，YOLOv11实现了最佳性能（86.8% mAP），在5至10米高度上10至30%的热融合被确定为最佳检测参数。互补的架构比较表明，虽然RF-DETR实现了最高精度（69.2% mAP），随后是Faster R-CNN（67.6%）、YOLOv11（64.2%）和RetinaNet（50.2%），但YOLOv11比基于Transformer的RF-DETR训练速度快17.7倍（41分钟对12小时），在操作部署中呈现出关键的精度-效率权衡。聚合的多时序训练数据集在季节特定方法上表现更优，提升了1.8至9.6%，表明模型从多样化的热条件中获益。反坦克（AT）地雷的检测精度达到61.9%，而反人员（AP）地雷为19.2%，反映了这两种弹药类别的大小差异和热质量差异。由于本研究考察的是表面地雷，其中热对比最大化，因此未来研究应量化不同深度和异质土壤类型中地雷的热对比效应。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
