SULAND_v2优化了无人机探测地雷的RGB数据集，提升检测模型在复杂环境下的可靠性。
SULAND v2: A Refined RGB Dataset and Deep Learning Object Detection Benchmark for UAV/UGV-Based SUrface LANDmine Detection Under Domain Shift
- 人工修正标注，解决原数据集漏标、错标和标签混乱问题
- 改进后模型在正常场景下检测准确率提升14.6-19.6个百分点
- 新基准可评估模型跨域适应能力，适合地雷探测系统研发者使用
RGB图像为无人机/无人车（UAV/UGV）表面地雷探测提供了低成本实用方案，但相关目标检测器研究仍不足。现有数据集缺乏跨架构对比与分布外（OOD）分析，且公开数据稀缺制约发展。原SULAND数据集存在标注缺失、误标、定位偏差、可见性标准不一、视觉伪影及时间标签不一致等问题，且分布外类别编号顺序错误。本文提出SULAND_v2，保留原始图像与划分，手动修正标注以确保完整性、精确定位、标签有效性与类别一致性。该数据集包含33,771张图像和12,433个边界框。我们对九类共35种检测器配置进行基准测试。标注优化使YOLOv8在分布内（IID）测试中mAP@50提升14.6-19.6个百分点，修复分布外类别编号后，平均YOLOv8 OOD mAP@50提升约25个百分点。在SULAND_v2上，YOLOv12-Small取得最高IID mAP@50（0.908），RF-DETR-Large表现最佳OOD性能（mAP@50=0.799，召回率=0.675）。结果表明，高分布内精度不代表实际部署可用性。SULAND_v2为基于RGB的排雷任务提供可靠基准，用于评估模型在域偏移下的鲁棒性。
原文摘要 · Abstract (English)
RGB imagery offers a practical, low-cost option for Unmanned Aerial/Ground Vehicle (UAV/UGV) survey support in surface-landmine detection, but object detectors remain underexplored in this safety-critical domain. Limited cross-architecture benchmarking and insufficient out-of-distribution (OOD) analysis obscure whether detectors generalize across deployment conditions. This challenge is amplified by the scarcity of public RGB landmine datasets, making SULAND a key benchmark for PFM-1 and PMA-2 detection. However, inspection reveals missing/false annotations, localization errors, inconsistent visibility criteria, visual artifacts, temporal labeling inconsistencies, and an inverted OOD class-ID convention in SULAND. We present SULAND_v2, a refined RGB surface-landmine dataset and benchmark. Preserving original images and splits, we manually revise annotations to ensure completeness, precise localization, label validity, and class consistency. SULAND_v2 contains 33,771 images and 12,433 bounding boxes. We benchmark 35 detector configurations across nine families. Annotation refinement improves YOLOv8 in-distribution (IID) test mAP@50 by 14.6-19.6 percentage points, while fixing the OOD class-ID convention increases mean YOLOv8 OOD mAP@50 by ~25 percentage points. On SULAND_v2, YOLOv12-Small achieves the highest IID mAP@50 (0.908), while RF-DETR-Large yields the strongest OOD performance (0.799 mAP@50, 0.675 recall). Our results demonstrate that high IID accuracy does not guarantee operational readiness. SULAND_v2 provides a reliable benchmark for evaluating domain-shift robustness in RGB-based mine-action survey support.
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。