用无人机热成像检测未爆弹药，构建多场景数据集并优化检测模型。
UAV Thermal Imagery for Inert Ordnance Screening: Multi Campaign Dataset Development,Object Detection, and Practical Recommendations
- 基于四次实地采集的热成像数据，构建含5855对标注图像的数据集。
- 在33米和15米飞行高度下，验证了YOLOv11l与RT-DETR-R50的检测性能。
- 提出协同采集热光影像、考虑地表多样性等实用建议，适配人道扫雷场景。
未爆弹药（UXO）持续限制全球受污染地区民众通行、农业活动、基础设施恢复及环境修复。本研究通过四次实地考察，在田纳西州不同季节采集了惰性地雷、弹药及其他军械的热成像数据，覆盖短草、高植被、碎石、木屑、岩石、堆肥及压实表面，飞行高度分别为33米和15米。最终构建包含5,855对热成像标注图像的源数据集，其中正样本918张，背景图4,937张。经保留全部正样本并下采样背景后，33米数据集含420张训练图与106张验证图，15米数据集含629张训练图与157张验证图。采用YOLOv11l与RT-DETR-R50算法训练并评估自动候选目标检测模型。实践建议包括：同步采集热成像与可见光影像，纳入多样化地表与背景图像，关注太阳辐射变化后的时段，平衡调查覆盖范围与目标像素表现，使用本地代表性数据校准模型，并保留专业人员人工复核。该方法旨在用于后续技术勘查或排爆评估的筛查与优先级排序，非独立清除手段。
原文摘要 · Abstract (English)
Unexploded ordnance (UXO) continues to restrict civilian access, agricultural activity, infrastructure recovery, and environmental remediation in contaminated areas around the world. This study created a multi campaign UAV thermal image data set of inert ordnance, developed a labeled image set from collected imagery, tested object detection models, and identified practical considerations for humanitarian mine action and demining applications. Data were collected during four field campaigns in Tennessee under summer and winter conditions using inert mines, munitions, and other ordnance placed in short grass, tall vegetation, gravel, mulch, rock, compost, and compacted surfaces. Thermal imagery was collected under flight altitutes of 33 m and 15 m. The final source inventory contained 5,855 thermal image label pairs, including 918 positive images and 4,937 background images. After retaining all positive images and downsampling background images, the 33 m dataset contained 420 training and 106 validation images, while the 15 m dataset contained 629 training and 157 validation images. YOLOV11l and RT-DETR-R50 algorithms were trained and evaluated to develop an automated candidate detection model. Practical recommendations include collecting thermal and RGB imagery together, incorporating varied surfaces and background only imagery, considering periods following changes in solar exposure, balancing survey coverage against target pixel representation, calibrating models with representative local data, and retaining qualified human review. The intended use is screening and prioritization for follow on technical survey or EOD assessment, and not a standalone clearance.
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。