用于惰性弹药筛查的无人机热成像：多场景数据集构建、目标检测及实用建议
UAV Thermal Imagery for Inert Ordnance Screening: Multi Campaign Dataset Development,Object Detection, and Practical Recommendations
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中文总结 AI 辅助
本研究构建了多场景无人机热成像惰性弹药数据集，测试YOLOV11l等算法用于目标检测，提出排雷应用的实用建议，为后续排爆评估提供筛查支持。
中文摘要 AI 辅助
未爆弹药（UXO）持续限制全球受污染地区的民用通行、农业活动、基础设施恢复及环境修复。本研究构建了多场景无人机热成像惰性弹药数据集，从采集的影像中生成标注图像集，测试了目标检测模型，并确定了用于人道主义排雷行动和排爆应用的实用注意事项。数据采集于田纳西州的四次野外场景，涵盖夏季和冬季条件，使用惰性地雷、弹药及其他弹药，放置于短草、高植被、砾石、覆盖物、岩石、堆肥及压实表面。热成像采集高度为33米和15米。最终源数据集包含5855对热成像标签，其中918张为正样本图像，4937张为背景图像。保留所有正样本图像并对背景图像进行下采样后，33米数据集包含420张训练图像和106张验证图像，15米数据集包含629张训练图像和157张验证图像。训练并评估了YOLOV11l和RT-DETR-R50算法，以开发自动候选检测模型。实用建议包括：同步采集热成像与RGB影像；纳入多样表面及仅含背景的影像；考虑太阳照射变化后的时段；平衡调查覆盖范围与目标像素占比；用代表性本地数据校准模型；保留合格人工审核。预期用途为后续技术调查或排爆评估的筛查与优先级排序，而非独立排爆。
英文摘要
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.
发表机构
- Oak Ridge National Laboratory（橡树岭国家实验室）
- UT-Battelle, LLC（UT-巴特勒有限责任公司）
- US Department of Energy（美国能源部）
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